SAS Macros Tutorial


The Toolkit for Weighting and Analysis of Nonequivalent Groups, TWANG, contains a set of macros to support causal modeling of observational data through the estimation and evaluation of propensity scores and associated weights (Ridgeway et al., 2013). The macros call functions from the twang package in the R environment for statistical computing and graphics (R Core Team, 2013). The twang package was developed in 2004. After extensive use, it received a major update in 2012. The SAS twang macros were developed in 2014 to support the use of the twang tools without requiring analysts to learn R.

The propensity score is the probability that a particular case would be assigned or exposed to a treatment condition. Rosenbaum & Rubin (1983) showed that knowing the propensity score is sufficient to separate the effect of a treatment on an outcome from observed confounding factors that influence both treatment assignment and outcomes, provided the necessary conditions hold. The propensity score has the balancing property that given the propensity score the distribution of features for the treatment cases is the same as that for the control cases. While the treatment selection probabilities are generally not known, good estimates of them can be effective at diminishing or eliminating confounds between pretreatment group differences and treatment outcomes in the estimation of treatment effects.

There are now numerous propensity scoring methods in the literature. They differ in how they estimate the propensity score (e.g. logistic regression, CART), the target estimand (e.g. treatment effect on the treated, population treatment effect), and how they utilize the resulting estimated propensity scores (e.g. stratification, matching, weighting, doubly robust estimators). We originally developed the twang package with a particular process in mind, generalized boosted regression, to estimate the propensity scores and weighting of the comparison cases to estimate the average treatment effect on the treated (ATT). However, we have updated the package to also meaningfully handle the case where interest lies in using the population weights (e.g., weighting of comparison and treatment cases to estimate the population average treatment effect, ATE.) The main workhorse of twang is the %ps macro which implements generalized boosted regression modeling to estimate the propensity scores and other tools that allow users to assess the success of the resulting weights at obtaining equivalence (or "balance") in the pretreatment covariate distributions of treatment and control groups. However, the framework and functions of the package are flexible enough to allow the user to use propensity score estimates from other methods and to assess the usefulness of those estimates for ensuring balance between treatment and control groups using tools from the package. The same set of macros is also useful for other tasks such as non-response weighting, as discussed in Section 4.

The twang macros aim to compute from the data estimates of the propensity scores which yield accurate causal effect estimates, check the quality of the weights by assessing whether or not they have the balancing properties that we expect in theory, and use them in computing treatment effect estimates.

An ATT example to start

2.1 Set-Up

If you have not already done so, you will need to download the twang macro file (“twang mac") and supporting documents to a folder on your computer. The files include:

  • twang mac — file containing the SAS macro code
  • twang mac help.txt — help file with details on implementing the macros
  • R startup.doc — step by step details on installing R
  • lalonde.sas7bdat — Example SAS dataset from the Lalonde Study
  • lindner.sas7bdat — Example SAS dataset from the Lindner Study
  • egsingle.sas7bdat — Example SAS dataset from the Raudenbush and Bryk Study
  • twang tutorial — SAS code from examples presented in this tutorial

The SAS datasets and example code will be useful for trying the code presented in this tutorial. Those files are not necessary for you to run your own applications.

To use the macros, you will need to include them in your SAS scripts

This is accomplished using a "%include" statement such as

%include "C:\Users\uname\SASFile\";

where the path, "C:\Users\uname\SASFile", is replaced by the path to where you have stored the macro file1.

The macros will run code in R and import the results into your SAS session. The macros will export the users' data to a .csv file that can be read into R. They will also create an R script file that is run in R batch mode. The script file exports weights and diagnostic information in .csv files that are then ported back into SAS. By default all the files created by the macro are stored in "SAStemp" directory that is deleted at the end of the SAS session. Users can redirect the storage of the intermediate output files through parameters in the macros. Details are presented below. To manage file transfer between SAS and R and ensure all the necessary R functions are working, the macros use features of Windows operating systems only available in Windows Vista or later.

The macros rely on the twang package in R. You will need to install R from from The Comprehensive R Archive Network ( The software can be installed by clicking on the link for the users computer platform (e.g., Windows users would click on "Download R for Windows" and then click on the "base" link to download the standard R software). For assistance on installing R please see the Start-up file Toolkit for Weighting and Analysis of Nonequivalent Groups SAS Macros Start-Up (PDF), or you can view tutorial videos on Youtube (such as "How to Install R" by YouTube user IntroToR).

Users will need to note the directory where the R software is installed and the name of the executable file. For Windows users with a 64-bit processor the directory information for the standard installment is

C:\Program FilesR\R-3.0.2\bin\x64

where 3.0.2 is replaced by the current version of R at the time of installation.2 For users with a 32-bit processor the directory information for the standard installment is

C:\Program FilesR\R-3.0.2\bin\i386

Again 3.0.2 is replaced by the current version of R at the time of installation. For both 64 or 32-bit processors, the executable is R.exe for batch implementation. Users with some familiarity with R might also want to install the twang package in R. The SAS macros can install the package if the user does not.

The macros also create a "TWANG" folder in the user's AppData\Local folder (C:\\Users\username \AppData\Local\TWANG would be the default for a user with the "username" as his or her username). The macros may use this folder to store files used in running the twang package in R or other files needed by the programs. This folder will remain on the user's hard drive until it is removed. Users can remove the folder using any method they would use for removing a folder or they can run the %remove_twang_folder macro when they no longer plan to use TWANG.

2.2 Estimating propensity scores with the %ps() macro

To demonstrate the package we utilize data from Lalonde's National Supported Work Demonstration analysis (Lalonde, 1986, Dehejia & Wahba, 1999, This dataset is provided with the twang macros (lalonde.sas7bdat).

For the lalonde dataset, the variable is the 0/1 treatment indicator, 1 indicates "treatment" by being part of the National Supported Work Demonstration and 0 indicates "comparison" cases drawn from the Current Population Survey. In order to estimate a treatment effect for this demonstration program that is unbiased by pretreatment group differences on other observed covariates, we include these covariates in a propensity score model of treatment assignment: age, education, Black, Hispanic, having no degree, married, earnings in 1974 (pretreatment), and earnings in 1975 (pretreatment). Note that we specify no outcome variables at this time. The %ps macro is the primary method in twang for estimating propensity scores. This step is computationally intensive and can take a few minutes.

%ps(treatvar=treat, vars=age educ black hispan nodegree married re74 re75,
stopmethod=es.mean ks.max,
sampw =,
estimand = ATT,
Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe,

The arguments to %ps require some discussion. The first argument is the treatment variable, which as noted earlier is treat. The next vars lists the names of the covariates to be used in model. Following the common syntax for SAS macros, the variable names are separated by spaces and not in quotation marks. The class argument list categorical variables. These variables should also be specified in the vars argument.

The next argument, dataset, indicates the dataset to be used in the analysis. It can be a permanent or temporary SAS dataset. In this example it is assumed to be stored in a permanent SAS dataset named "lalonde" in a folder identified by the libname "sasin". R, and in particular, the twang package in R, can be slow and require large amounts of memory to process large datasets. The user might get better computational performance by including only the variables to be used by %ps in the dataset specified by the dataset parameter.

ntrees, intdepth, and shrinkage are parameters for the GBM that %ps computes. The argument ntrees is the maximum number of iterations that the GBM will run. There will be a warning in the SAS log if the estimated optimal number of iterations is too close to the bound selected in this argument because it indicates that balance may improve if more complex models (i.e., those with more trees or a larger value for ntrees) are considered. The user should increase ntrees or decrease shrinkage and rerun the macro if this warning appears. The argument intdepth controls the level of interactions allowed in the GBM. The default is 3 and we typically use the default in our analyses. The GBM estimation algorithm uses shrinkage to enhance the smoothness of resulting model. The shrinkage argument controls the amount of shrinkage. Small values such as 0.005 or 0.001 yield smooth fits but require greater values of ntrees to achieve adequate fits. Computational time increases inversely with shrinkage argument. Additional details on ntrees, intdepth, and shrinkage can be found in McCaffrey, Ridgeway, and Morral (2004).

permtestiters specifies whether p-values for KS statistics should be calculated using Monte Carlo methods, which is slow but accurate, or estimated using an analytic approximation that is fast, but produces poor estimates in the presence of many ties. If permtestiters=0 (default) is called, then analytic approximations are used. If permtestiters=500 is called, then 500 Monte Carlo trials are run to establish the reference distribution of KS statistics for each covariate. Higher numbers of trials will produce more precise p-values for the test of the KS statistic. Specifying permtestiters greater than zero can greatly slow down the twang computations. We tend to rely on the approximations (permtestiters=0) when using twang in practice.

The stopmethod argument specifies a set (or sets) of rules and measures for assessing the balance, or equivalence, established on the pretreatment covariates of the treatment and weighted control group. The iterations used in the GBM minimize the differences between the treatment and control groups as measured by the balance statistics specified by values given to stopmethod argument. The package includes four built-in stop methods. They are "es.mean", "es.max", "ks.mean", and "ks.max". The four stopping rules are defined by two components: a balance metric for covariates and rule for summarizing across covariates. The balance metric summarizes the difference between two univariate distributions of a single pre-treatment variable (e.g., age). The default stopping rules in twang use two balance metrics: absolute standardized bias (also referred to as the absolute standardized mean difference of the Effect Size) and the Kolmogorov-Smirnov (KS) statistic. The stopping rules use two different rules for summarizing across covariates: the mean of the covariate balance metrics ("mean") or the maximum of the individual covariate balance metrics ("max"). The first piece of the stopping rule name identifies the balance metric ("es" for the effect size or standardized bias or "ks" for the KS statistic) and the second piece specifies the method for summarizing across balance metrics ("mean" or "max"). For instance, "es.mean" uses the effect size or the absolute standardized bias and summarizes across variables with the mean and the "ks.max" uses the KS statistics to assess balances and summarizes using the maximum across variables and the other two stopping rules use the remaining two combinations of balance metrics and summary statistics. The balance metrics depend on the estimand and correct specification of the metrics is set automatically by the specification of the estimand argument in the %ps macro.

The sampw argument is the name of the variable that contains sampling weights if they exist. If there are no sampling weights the parameter can be left unspecified as it is in this example.

The estimand argument is used to indicate whether the analyst is interested in estimating the average treatment effect (ATE) or the average treatment effect on the treated (ATT), as we do above. ATE addresses the question of how outcomes would differ if everyone in the sample were given the treatment versus everyone being given the control (Wooldridge, 2002). ATT, on the other hand, estimates the analogous quantity averaging only over the subjects who were actually treated.

The primary results of the %ps macro are the weights which can be used for estimating effects. It also produces checks of the balance of covariates returned to the SAS lst in the form of balance tables and an overall summary table. It will also produce diagnostic plots to help assess the balance and the GBM fit, if the user requests them by specifying the plotname argument.

By default, the propensity score based weights created by the %ps macro are saved in a temporary SAS dataset (_inputds) in the work folder. Users can save this file in permanent dataset using a datastep or they can specify an alternative temporary or a permanent dataset using the output_dataset argument. In the example, we save the output dataset in "lalonde_att_wgts". The output dataset contains the data from the dataset specified in data with the weight variables attached. There is one weight variable for each stopping rule specified in stopmethod. The weight variables are named according to the stopping rule and estimand so that in this example there is a weight variable es_mean_ATT with the weights from a GBM with the iterations chosen to minimize the mean standardized bias (effect size) and a second weight variable ks_max_ATT with the weights from a GBM with the iterations chosen to minimize the maximum KS statistic. Because the estimand is ATT, the weight equals 1 for every individual in the treatment group. This dataset can be saved and used to estimate the treatment effects.

The argument Rcmd specifies the R program executable file for running R. The location of the file is determined through the installation of R. The default the default setup of R Version 3.0.2 on Windows 7 resulted in the executable being "C:\Program Files\R\R-3.0.2\bin\x64\R.exe" For other versions of R, 3.0.2 will be replaced by the version number. If the analyst has added R to the path environmental variable then the path does not need to be included in the Rcmd argument, "Rcmd=R" will work.3 (Similarly specifying the ".exe" extension is not necessary.)

The argument plotname gives the name for a pdf file of default diagnostic plots that twang creates. Creation of the plots is optional. If plotname is not given or if left blank then no plots are created. If the argument contains a path the pdf file with plots will be stored in the folder specified by it. Otherwise the file will be stored in the folder specified by objpath, if it is specified. If a path is not include in plotname value and objpath is not set then the file with plots is stored in the directory where SAS was launched (BATCH mode SAS) or the user's home directory (interactive SAS). In our call to %ps we do not include the path in the plotname (lalonde_twang.pdf) but we do specify the objpath as "c:\Users\uname\twang_example" so the plots will be stored in that folder. The analyst will need to specify a folder where they have permission to create a file if objpath is used.

The final argument objpath specifies a folder where files created by the macro to run the twang functions in R and return the results to SAS are stored. Namely, an "R object" ("ps.RData") with the GBM and a log of the R session ("ps.Rout"). The R object is required for running the %plot macro. objpath is optional. If it is not specified, then the macro writes intermediate files and R object and log the SAStmp directory and all the files are deleted at the end of the SAS session. If the analyst wants to review the log or use the R object objpath must be specified. If objpath does not include a full path then the specified folder is assumed to exist in the folder where SAS was launched (BATCH SAS) or the user's home directory (interactive SAS).

Having fit the GBM, the analyst should perform several diagnostic checks before estimating the causal effect in question. The first of these diagnostic checks makes sure that the specified value of ntrees allowed the GBM to explore sufficiently complicated models. We can do this quickly using the "convergence" or "optimization" plot created by the R functions. There are two ways to generate this plot. The first way to obtain the plot is to specify plotname argument in %ps. This will create all the default diagnostics plots available in twang. They will be stored in the single pdf file specified by the argument. In this example the file is: "C:\ Users\uname\twang\_example\lalonde\_twang.pdf"

The default file created by specifying the plotname contains one page for each type of diagnostic plot and each page contains a multi-panel plot with one panel for each stopping rule specified in the stopmethod argument. If only one stopping rule is specified each page contains a single panel. Figure 1 presents the plots for the first page of the %ps call.

figure 1

Example of an optimization plot for two stopping rules ("es.mean," and "ks.max") for estimating ATT weights for the Lalonde dataset. It was generated by setting the plotname argument in the %ps() macro and appears as the first page of a multiple page document of diagnostics plots

The second way to obtain the plot is to run the %plot macro which can create specific plots and store them using pdf or other file formats. As noted above, the objpath argument must be specified in %ps to use the %plot macro. The inputobj argument specifies the R object created by the ps macro. If the argument does not include a path the file is assumed be stored in the folder where SAS was launched. plotname gives the file name for the plot and plotformat specifies the file format. Allowable file formats are: jpg - JPEG, pdf - PDF, png - PNG, wmf - Windows enhanced metafile, and ps - postscript. The argument plots specifies the diagnostic plot to be created. Only one type of diagnostic plot can be created by a %plot call.4 The plot can be specified by number or name and the names should not be in quotation marks. The convergence plot is specified by "1" or "optimize". The results of the following call are in Figure 2.

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

The plot macro also allows for restricting the plot to results for a single stopping rule by specifying it by number in the subset argument. Stopping rules are numbered by alphabetical order; not the order in which they are specified.

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

figure 2

Figure 2: Example of an optimization plot for a single stopping rule (ks.max) for estimating ATT weights for the Lalonde dataset.

The convergence plot plots the balance measures as a function of the number of iterations in the GBM algorithm, with higher iterations corresponding to more complicated fitted models. In this example, 2127 iterations minimized the average effect size difference and 1756 iterations minimized the largest of the eight Kolmogorov-Smirnov (KS) statistics computed for the covariates. This can be observed in Figure 2. The maximum of KS statistics starts large, decreases and then increases somewhere between 1000 and 2000 iterations. The plot suggest ntrees=5000 was sufficient. However, if it had appeared that additional iterations would be likely to result in lower values of the balance statistic — for instance, if the maximum was still declining without appearing to have attained a minimum by the maximum number of iterations, ntrees should be increased. As shown in the plot, after a point, additional complexity typically makes the balance worse. This figure also gives information on how compatible two or more stopping rules are: if the minima for multiple stopping rules under consideration are near one another, the results should not be sensitive to which stopping rule one uses for the final analysis. See Section 5.3 for a discussion of these and other balance measures.

Assessing "balance" using balance tables

The %ps macro returns a balance table in the temporary SAS data set _baltab, in the work folder, and writes the table to the list file or window.5 This table shows how well the resulting weights succeed in manipulating the control group so that its weighted pretreatment characteristics match, or balance, those of the unweighted treatment group if estimand = "ATT" or in adjusting both the control and treatment groups so that their weighted pretreatment characteristics match, or balance, with one another if estimand = "ATE". The balance table includes information on the pretreatment covariates before and after weighting. The results for the unweighted analysis (are identified by "unw"). Those for each of the specified values to stopmethod are identified by the stop method label with the specified estimand appended, here "es.mean.ATT" and "ks.max.ATT." There is no research on which stopping rule is best and the choice is likely to depend on the application. McCaffrey et al. (2004) essentially used "es.mean" for the analyses, but our more recent work has sometimes used "ks.max". See McCaffrey et al. (2013) for a greater detais on stopping rules.

If there are missing values (represented as NA) in the covariates, twang will attempt to construct weights that also balance rates of missingness in the treatment and control arms. In this case, the balance table will have an extra row for each variable that has missing entries. The columns of the table consist of the following items:

row_name - The covariate variable name with the stopping rule or unw added as a prefix.

tx_mn, ct_mn - The treatment means and the control means for each of the variables. The unweighted table (unw) shows the unweighted means. For each stopping rule the means are weighted using weights corresponding to the gbm model selected by %ps using the stopping rule. When estimand = "ATT" the weights for the treatment group always equal 1 for all cases and there is no difference between unweighted and propensity score weighted

tx_sd, ct_sd - The propensity score weighted treatment and control groups' standard deviations for each of the variables. The unweighted table (unw) shows the unweighted standard deviations

std_e_sz - The standardized effect size, defined as the treatment group mean minus the control group mean divided by the treatment group standard deviation if estimand = "ATT" or divided by the pooled sample (treatment and control) standard deviation if estimand = "ATE". (In discussions of propensity scores this value is sometimes referred to as "standardized bias".) Occasionally, lack of treatment group or pooled sample variance on a covariate results in very large (or infinite) standardized effect sizes. For purposes of analyzing mean effect sizes across multiple covariates, we set all standardized effect sizes larger than 500 to NA (missing values)

stat, p - Depending on whether the variable is continuous or categorical, stat is a t-statistic or a χ2 statistic. p is the associated p-value

ks, ksffpval - The Kolmogorov-Smirnov test statistic and its associated p-value. P-values for the KS statistics are either derived from Monte Carlo simulations or analytic approximations, depending on the specifications made in the permtestiters argument of the ps macro. For categorical variables this is just the χ2 test p-value.

SAS Output: Balance Table Results

table 1

The ps macro also returns a compact summary of the sample sizes of the groups and the balance measures in a temporary SAS data file _summ and writes it to the list file or window.6 If permtestiters > 0 was used in the call to %ps, then Monte Carlo simulation is used to estimate p-values for the maximum KS statistic that would be expected across the covariates, had individuals with the same covariate values been assigned to groups randomly. Thus, a p-value of 0.04 for max.ks.p indicates that the largest KS statistic found across the covariates is larger than would be expected in 96% of trials in which the same cases were randomly assigned to groups.

Output: Summary Table Results

table 2

In general, weighted means can have greater sampling variance than unweighted means from a sample of equal size. The effective sample size (ESS) of the weighted comparison group captures this increase in variance as

Output: Summary Table Results

equation one

where summation is over cases in the control group. The ESS is approximately the number of observations from a simple random sample that yields an estimate with sampling variation equal to the sampling variation obtained with the weighted comparison observations. Therefore, the ESS will give an estimate of the number of comparison participants that are comparable to the treatment group when estimand = "ATT". When the estimand of interest is "ATE", there is an analogous ESS for the treatment group because the weights are no longer equal to one for that group. The ESS is an accurate measure of the relative size of the variance of means when the weights are fixed or they are uncorrelated with outcomes. Otherwise the ESS underestimates the effective sample size (Little & Vartivarian, 2004). With propensity score weights, it is rare that weights are uncorrelated with outcomes. Hence the ESS typically gives a lower bound on the effective sample size, but it still serves as a useful measure for choosing among alternative models and assessing the overall quality of a model, even if it provides a possibly conservative picture of the loss in precision due to weighting.

Graphical assessments of balance

The %plot macro can generate useful diagnostic plots to evaluate the propensity scores. The full set of plots available in twang and the argument value of plot to produce each one are given in Table [tab:plots]. The convergence or optimization plot was discussed above. Other diagnostic plots are specified by the value of the plots argument. For example, specifying plots = 2 or plots = boxplot produces boxplots illustrating the spread of the estimated propensity scores in the treatment and comparison groups. Whereas propensity score stratification requires considerable overlap in these spreads, excellent covariate balance can often be achieved with weights, even when the propensity scores estimated for the treatment and control groups show little overlap.

Output: Summary Table Results

plot table 1

Table 1: Available options for plots argument to %plot macro.

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

(Recall that wherever the example code has "C:\Program Files\R\R-3.0.2\bin\x64" it must be replaced by that actual path where the R.exe is stored.)

figure 3

Figure 3: Example of the default diagnostic boxplot of propensity scores from the %plot macro for estimating ATT weights for the Lalonde dataset.

The effect size plot (see Figure 4) illustrates the effect of weights on the magnitude of differences between groups on each pretreatment covariate. These magnitudes are standardized using the standardized effect size described earlier. In these plots, substantial reductions in effect sizes are observed for most variables (blue lines), with only one variable showing an increase in effect size (red lines), but only a seemingly trivial increase. Closed red circles indicate a statistically significant difference, many of which occur before weighting, none after. In some analyses variables can have very little variance in the treatment group sample or the entire sample and group differences can be very large relative to the standard deviations. In these situations, the user is warned that some effect sizes are too large to plot.

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

figure 4

Figure 4: Example of the default diagnostic standardized effect size plot from the %plot macro for estimating ATT weights for the Lalonde dataset.

When many of the p-values testing individual covariates for balance are very small, the groups are clearly imbalanced and inconsistent with what we would expect had the groups been formed by random assignment. After weighting we would expect the p-values to be larger if balance had been achieved. We use a QQ plot comparing the quantiles of the observed p-values to the quantiles of the uniform distribution (45 degree line) to conduct this check of balance. Ideally, the p-values from independent tests in which the null hypothesis is true will have a uniform distribution. Although the ideal is unlikely to hold even if we had random assignment (Bland, 2013), severe deviation of the p-values below the diagonal suggests lack of balance and p-values running at or above the diagonal suggests balance might have been achieved. The p-value plot(plots=4) allows users to visually to inspect the p-values of the t-tests for group differences in the covariate means.

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

figure 5

Figure 5: Example of the default diagnostic t-test p-value plot from the %plot macro for estimating ATT weights for the Lalonde dataset.

Figure 5, presents the t-test p-value plot for the Lalonde example. Before weighting (closed circles), the groups have statistically significant differences on many variables (i.e., p-values are near zero). After weighting (open circles) the p-values are generally above the 45-degree line, which represents the cumulative distribution of a uniform variable on [0,1]. This indicates that the p-values are even larger than would be expected in an ideal randomized study, so that balance is generally good. One can inspect similar plots for the KS statistic with the argument plots = 5 or "ks". (see Figure 6)

Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe);

figure 6

Figure 6: Example of the default diagnostic Kolmogorov-Smirnov test p-value plot from the %plot macro for estimating ATT weights for the Lalonde dataset.

Analysis of outcomes

The aim of the National Supported Work Demonstration analysis is to determine whether the program was effective at increasing earnings in 1978. We will estimate this effect as the difference in the treatment and weighted control group means and test that it is not zero using a Wald test. The propensity score adjusted test can be computed with PROC SURVEYREG. We start with an analysis using the weights derived from the GBM selected to minimize the mean standardized bias (“es.mean” stopping rule).

proc surveyregdata=lalonde_att_wgts;
model re78 = treat;
weight es_mean_att;


                                              13:21 Friday, January 3, 2014   8
                            The SURVEYREG Procedure
table 3

The analysis estimates an increase in earnings of $733 for those that participated in the NSW compared with similarly situated people observed in the CPS. The effect, however, does not appear to be statistically significant.

Some authors have recommended utilizing both propensity score adjustment and additional covariate adjustment to minimize mean square error or to obtain "doubly robust" estimates of the treatment effect (Huppler-Hullsiek & Louis 2002, Bang & Robins 2005). These estimators are consistent if either the propensity scores are estimated correctly or the regression model is specified correctly. For example, note that the balance table for ks.max.ATT made the two groups more similar on nodegree, but still some differences remained, 70.8% of the treatment group had no degree while 60.9% of the comparison group had no degree. While linear regression is sensitive to model misspecification when the treatment and comparison groups are dissimilar, the propensity score weighting has made them more similar, perhaps enough so that additional modeling with covariates can adjust for any remaining differences. In addition to potential bias reduction, the inclusion of additional covariates can reduce the standard error of the treatment effect if some of the covariates are strongly related to the outcome.

proc surveyreg data= lalonde_att_wgts;
model re78 = treat
nodegree; weight es_mean_att;

SAS Output: PROC SURVEYREG Results, One Covariate

table 5

Adjusting for the remaining group difference in the nodegree variable slightly increased the estimate of the program’s effect to $920, but the difference is still not statistically significant. We can further adjust for the other covariates, but that too in this case has little effect on the estimated program effect.

proc surveyreg data=lalonde_att_wgts;
model re78 = treat age educ black hispan nodegree married re74 re75;
weight es_mean_att;

SAS Output: PROC SURVEYREG Results, All Covariates

The SURVEYREG Procedure

Regression Analysis for Dependent Variable re78

Data Summary

Number of Observations 614
Sum of Weights 329.58393
Weighted Mean of re78 6027.8
Weighted Sum of re78 1986665.6

Fit Statistics

R-square 0.05578
Root MSE 6917.62
Denominator DF 613

Tests of Model Effects

Effect Num DF F Value Pr > F

Model 9 2.49 0.0086
Intercept 1 0.32 0.5696
treat 1 0.55 0.4601
age 1 0.00 0.9572
educ 1 8.20 0.0043
black 1 0.56 0.4549
hispan 1 0.13 0.7232
nodegree 1 0.11 0.7441
married 1 0.21 0.6490
re74 1 0.10 0.7535
re75 1 0.64 0.4242

NOTE: The denominator degrees of freedom for the F tests is 613.

Estimated Regression Coefficients

Parameter Estimate Error t Value Pr > |t|

Intercept -2458.7982 4321.26255 -0.57 0.5696
treat 758.4891 1026.06245 0.74 0.4601
age 3.0047 55.99413 0.05 0.9572
educ 748.8193 261.55961 2.86 0.0043
black -762.6813 1019.88558 -0.75 0.4549
hispan 610.6271 1723.24917 0.35 0.7232
nodegree 535.0055 1638.20712 0.33 0.7441
married 491.7587 1079.99447 0.46 0.6490
re74 0.0570 0.18142 0.31 0.7535

Estimating the program effect using linear regression

Users may be wondering whether using twang and weighting to adjust for differences between groups yields different results than the more familiar regression approaches to adjusting for group differences on observed covariates. We now compare our weighted estimates of the program effect to results from a more traditional analysis in which the program effect is estimated by a linear model with a treatment indicator and linear terms for each of the covariates. PROC REG is the standard procedure for fitting such models in SAS. Recall that "sasin" is the for the folder where the Lalonde dataset set is stored.

proc reg data=sasin.lalonde;
model re78 = treat age educ black hispan nodegree married re74 re75;

SAS Output: PROC REG Results, All Covariates

                                               13:37 Friday, January 3, 2014  16

The REG Procedure
Model: MODEL1
Dependent Variable: re78

Number of Observations Read 614
Number of Observations Used 614

Analysis of Variance

Sum of Mean
Source DF Squares Square F Value Pr > F

Model 9 5055423967 561713774 11.64 <.0001
Error 604 29157220815 48273544
Corrected Total 613 34212644782

Root MSE 6947.91655 R-Square 0.1478
Dependent Mean 6792.83448 Adj R-Sq 0.1351
Coeff Var 102.28302

Parameter Estimates

Parameter Standard
Variable DF Estimate Error t Value Pr > |t|

Intercept 1 66.51452 2436.74580 0.03 0.9782
treat 1 1548.24380 781.27930 1.98 0.0480
age 1 12.97763 32.48891 0.40 0.6897
educ 1 403.94123 158.90624 2.54 0.0113
black 1 -1240.64408 768.76441 -1.61 0.1071
hispan 1 498.89686 941.94255 0.53 0.5966
nodegree 1 259.81737 847.44205 0.31 0.7593
married 1 406.62085 695.47232 0.58 0.5590
re74 1 0.29638 0.05827 5.09 <.0001
re75 1 0.23153 0.10462 2.21 0.0273

This model estimates a rather strong treatment effect, estimating a program efect of $1548 with a p-value=0.048. Several variations of this regression approach also estimate strong program effects. For example using square root transforms on the earnings variables yields a p- value=0.016. These estimates, however, are very sensitive to the model structure since the treatment and control subjects differ greatly as seen in the unweighted balance comparison (unw) from the balance table.

Propensity scores estimated from logistic regression

Propensity score analysis is intended to avoid problems associated with the misspecification of covariate adjusted models of outcomes, but the quality of the balance and the treatment effect estimates can be sensitive to the method used to estimate the propensity scores. For instance, we consider estimating the propensity scores using logistic regression instead of the %ps macro and compare the results to the weights from twang.

proc logistic data=sasin.lalonde descending; model treat = age educ black hispan nodegree married re74 re75;
output out=llogit pred=phat;

PROC LOGISTIC fits the logistic regression model. The descending option specifies that the model fit the probability that treat=1. The default is to model the probability that it equals zero. The output statement specifies that predicted probabilities (keyword pred) be saved in a variable named phat to a temporary SAS dataset llogit. The dataset includes all the variables in the input dataset and appends the predicted probabilities. We can create the ATT weights in a datastep as shown below with weights for the treatment group equal to one and weights for the control group equal to the odds of treatment.

data llogit;
set llogit;
w_logit_att = treat + (1-treat)*phat/(1-phat);

The %dxwts macro provides the balance assessment tools of twang for weights generated using any method, not just by %ps. The arguments are similar to those in %ps except weight variables are now specified and must by part of the dataset specified by the dataset argument. Multiple weights can be assessed but they must all be set for a common estimand. The macro produces summary and balance tables that are just like those produced by the %ps macro except there is no iter variable in the summary because the weights might not come from GBM model.

vars=age educ black hispan nodegree married re74 re75,
Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe)

SAS Output: %dxwts Summary and Balance Tables

                                                                      dxwts Summary table                                      16:48 Monday, January 6, 2014  19

Obs type n_treat n_ctrl ess_treat ess_ctrl max_es mean_es max_ks mean_ks

1 unw 185 429 185 429 1.7567745147 0.5687258912 0.6404460404 0.2702450702
2 w_logit_att 185 429 185 99.8153887 0.1188495948 0.0318840912 0.3078038985 0.0930231893

dxwts Balance table: w_logit_att 16:48 Monday, January 6, 2014 20

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

9 w_logit_att.age 25.816 7.155 24.966 10.535 0.119 0.739 0.46 0.308 0 w_logit_att
10 w_logit_att.educ 10.346 2.011 10.403 2.459 -0.028 -0.219 0.827 0.036 1 w_logit_att
11 0.843 0.365 0.845 0.362 -0.006 -0.069 0.945 0.002 1 w_logit_att
12 w_logit_att.hispan 0.059 0.237 0.059 0.236 0.001 0.008 0.993 0 1 w_logit_att
13 w_logit_att.nodegree 0.708 0.456 0.69 0.463 0.04 0.332 0.74 0.018 1 w_logit_att
14 w_logit_att.married 0.189 0.393 0.171 0.377 0.047 0.456 0.649 0.019 1 w_logit_att
15 w_logit_att.re74 2095.574 4886.62 2106.045 4235.833 -0.002 -0.022 0.983 0.228 0.002 w_logit_att
16 w_logit_att.re75 1532.055 3219.251 1496.541 2716.258 0.011 0.107 0.915 0.133 0.185 w_logit_att

dxwts Balance table: unw 16:48 Monday, January 6, 2014 21

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

1 unw.age 25.816 7.155 28.03 10.787 -0.309 -2.994 0.003 0.158 0.003 unw
2 unw.educ 10.346 2.011 10.235 2.855 0.055 0.547 0.584 0.111 0.074 unw
3 0.843 0.365 0.203 0.403 1.757 19.371 0 0.64 0 unw
4 unw.hispan 0.059 0.237 0.142 0.35 -0.349 -3.413 0.001 0.083 0.317 unw
5 unw.nodegree 0.708 0.456 0.597 0.491 0.244 2.716 0.007 0.111 0.074 unw
6 unw.married 0.189 0.393 0.513 0.5 -0.824 -8.607 0 0.324 0 unw
7 unw.re74 2095.574 4886.62 5619.237 6788.751 -0.721 -7.254 0 0.447 0 unw
8 unw.re75 1532.055 3219.251 2466.484 3291.996 -0.29 -3.282 0.001 0.288 0 unw

For weights estimated with logistic regression, the largest KS statistic was reduced from the unweighted sample’s largest KS of 0.64 to 0.31, which is still quite a large KS statistic. The means of the two groups appear to be quite similar while the KS statistic shows substantial differences in their distributions.

The below table compares the balancing quality of the weights directly with one another.

n_treat ess_ctrl max_es mean_es max_ks mean_ks
unw 185 429.00 1.76 0.57 0.64 0.27
logit 185 99.82 0.12 0.03 0.31 0.09
es.mean.ATT 185 22.96 0.22 0.08 0.12 0.06
ks.max.ATT 185 27.05 0.23 0.08 0.11 0.06

proc surveyreg data=llogit;
model re78 = treat;
weight w_logit_att;

The analysis estimates an increase in earnings of $1214 for those that participated in the NSW compared with similarly situated people observed in the CPS. We can also run a model with linear adjustments for the covariates combined with the logistic regression weights. Table [tab:effects] compares all of the treatment effect estimates.

Treatment effect PS estimate Linear adjustment
$733 GBM, minimize es none
$920 GBM, minimize es nodegree
$758 GBM, minimize es all
$1548 None all
$1214 Logistic regression none
$1237 Logistic regression all

3. An ATE example

In the analysis of Section 2, we focused on estimating ATT for the lalonde dataset. In that example, the ATE is not of great substantive interest because not all people who are offered entrance into the program could be expected to take advantage of the opportunity. Further, there is some evidence that the treated subjects were drawn from a subset of the covariate space. In particular, in an ATE analysis, we see that we are unable to achieve balance, especially for the black indicator.

We now turn to an ATE analysis that is feasible and meaningful. We focus on the lindner dataset, which was included in the USPS package in R (Obenchain 2011), and is included with the twang macros in the SAS data set lindner.sas7bdat. A tutorial by Helmreich and Pruzek (2009; HP) for the PSAgraphics package also uses propensity scores to analyze a portion of these data. HP describe the data as follows on p. 3 with our minor recodings in square braces:

The lindner data contain data on 996 patients treated at the Lindner Center, Christ Hospital, Cincinnati in 1997. Patients received a Percutaneous Coronary Intervention (PCI). The data consists of 10 variables. Two are outcomes: [sixMonthSurvive] ranges over two values... depending on whether patients surved to six months post treatment [denoted by TRUE] or did not survive to six months [FALSE]... Secondly, cardbill contains the costs in 1998 dollars for the first six months (or less if the patient did not survive) after treatment... The treatment variable is abcix, where 0 indicates PCI treatment and 1 indicates standard PCI treatment and additional treatment in some form with abciximab. Covariates include acutemi, 1 indicating a recent acute myocardial infarction and 0 not; ejecfrac for the left ventricle ejection fraction, a percentage from 0 to 90; ves1proc giving the number of vessels (0 to 5) involved in the initial PCI; stent with 1 indicating coronary stent inserted, 0 not; diabetic where 1 indicates that the patient has been diagnosed with diabetes, 0 not; height in centimeters and female coding the sex of the patent, 1 for female, 0 for male.

HP focus on cardbill — the cost for the first months after treatment — as their outcome of interest. However, since not all patients survived to six months, it is not clear whether a lower value of cardbill is good or not. For this reason, we choose six-month survival (sixMonthSurvive) as our outcome of interest.

Ignoring pre-treatment variables, we see that abcix is associated with lower rates of 6-month mortality:

proc freq data=sasin.lindner;
table sixMonthSurvive * abcix / chisq;
title "ATE Tutorial";

SAS Output: Frequency Table

ATE Tutorial 1
22:29 Monday, January 6, 2014

The FREQ Procedure

Table of sixMonthSurvive by abcix

sixMonthSurvive abcix

Percent |
Row Pct |
Col Pct | 0| 1| Total
FALSE | 15 | 11 | 26
| 1.51 | 1.10 | 2.61
| 57.69 | 42.31 |
| 5.03 | 1.58 |
TRUE | 283 | 687 | 970
| 28.41 | 68.98 | 97.39
| 29.18 | 70.82 |
| 94.97 | 98.42 |
Total 298 698 996
29.92 70.08 100.00

Statistics for Table of sixMonthSurvive by abcix

Statistic DF Value Prob
Chi-Square 1 9.8207 0.0017
Likelihood Ratio Chi-Square 1 8.8530 0.0029
Continuity Adj. Chi-Square 1 8.5077 0.0035
Mantel-Haenszel Chi-Square 1 9.8108 0.0017
Phi Coefficient 0.0993
Contingency Coefficient 0.0988
Cramer's V 0.0993

The question is whether this association is causal. If health care policies were to be made on the basis of these data, we would wish to elicit expert opinion as to whether there are likely to be other confounding pretreatment variables. For this tutorial, we simply follow HP in choosing the pre-treatment covariates. The twang model is fit as follows

vars=stent height female diabetic acutemi ejecfrac ves1proc,
estimand = ATE,
Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe,

We set estimand = ATE because we are interested in the effects of abciximab on everyone in the population. We do not specify the stopping rules. Consequently %ps uses the defaults: “es.mean” and “ks.max”. We then inspect pre- and post-weighting balance using the balance table.

SAS Output: Balance Tables for abcix

                                                                       Balance table: unw                                      22:59 Monday, January 6, 2014   3

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

1 unw.stent 0.705 0.456 0.584 0.494 0.257 3.624 0 0.121 0.004 unw
2 unw.height 171.443 10.695 171.446 10.589 0 -0.005 0.996 0.025 0.999 unw
3 unw.female 0.331 0.471 0.386 0.488 -0.115 -1.647 0.1 0.055 0.531 unw
4 unw.diabetic 0.205 0.404 0.268 0.444 -0.152 -2.127 0.034 0.064 0.349 unw
5 unw.acutemi 0.179 0.384 0.06 0.239 0.338 5.923 0 0.119 0.005 unw
6 unw.ejecfrac 50.403 10.419 52.289 10.297 -0.181 -2.64 0.008 0.114 0.008 unw
7 unw.ves1proc 1.463 0.706 1.205 0.48 0.393 6.693 0 0.188 0 unw

Balance table: ks.mean.ATE 22:59 Monday, January 6, 2014 4

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

8 ks.mean.ATE.stent 0.683 0.466 0.657 0.475 0.054 0.718 0.473 0.025 1 ks.mean.ATE
9 ks.mean.ATE.height 171.47 10.55 171.591 10.589 -0.011 -0.155 0.877 0.015 1 ks.mean.ATE
10 ks.mean.ATE.female 0.338 0.473 0.345 0.476 -0.015 -0.2 0.841 0.007 1 ks.mean.ATE
11 ks.mean.ATE.diabetic 0.215 0.411 0.229 0.421 -0.033 -0.432 0.666 0.014 1 ks.mean.ATE
12 ks.mean.ATE.acutemi 0.148 0.355 0.107 0.31 0.12 1.331 0.183 0.04 0.935 ks.mean.ATE
13 ks.mean.ATE.ejecfrac 51.052 10.333 51.604 9.11 -0.056 -0.798 0.425 0.027 0.999 ks.mean.ATE
14 ks.mean.ATE.ves1proc 1.395 0.666 1.337 0.573 0.094 1.203 0.229 0.028 0.999 ks.mean.ATE

Balance table: es.mean.ATE 22:59 Monday, January 6, 2014 5

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

15 es.mean.ATE.stent 0.683 0.466 0.653 0.477 0.063 0.849 0.396 0.03 0.996 es.mean.ATE
16 es.mean.ATE.height 171.468 10.534 171.501 11.04 -0.003 -0.039 0.969 0.018 1 es.mean.ATE
17 es.mean.ATE.female 0.337 0.473 0.35 0.478 -0.028 -0.38 0.704 0.013 1 es.mean.ATE
18 es.mean.ATE.diabetic 0.214 0.411 0.237 0.426 -0.055 -0.719 0.473 0.023 1 es.mean.ATE
19 es.mean.ATE.acutemi 0.148 0.355 0.11 0.313 0.114 1.303 0.193 0.038 0.95 es.mean.ATE
20 es.mean.ATE.ejecfrac 51.008 10.389 51.386 9.396 -0.038 -0.532 0.595 0.027 0.999 es.mean.ATE
21 es.mean.ATE.ves1proc 1.398 0.67 1.35 0.589 0.076 0.951 0.342 0.023 1 es.mean.ATE

This balance table shows that stent, acutemi, ejectfrac and ves1proc were all significantly imbalanced before weighting. After weighting (using either stop.method considered) we do not see problems in this regard. Examining the diagnostic plots created by the specification of the plotname does not reveal problems, either. In regard to the optimize plot, we note that the scales of the KS and ES statistics presented in the optimize plots are not necessarily comparable. The fact that the KS values are lower than the ES values in the optimize plot does not suggest that the KS stopping rule is finding superior models. Each panel of the optimize plot indicates the gbm that minimizes each stopping rule. The panels should not be compared other than to compare the number of iterations selected by each rule.

From the summary table generated by %ps, we see that the “es.mean.ATE” stopping rule results in a slightly higher ESS with comparable balance measures, so we proceed with those weights. Also, we note that ess.treat is no longer equal to n.treat since we are focusing on ATE rather than ATT.

SAS Output: Summary Table for abcix

                                                                         Summary table                                        06:50 Tuesday, January 7, 2014   2

Obs row_name n_treat n_ctrl ess_treat ess_ctrl max_es mean_es max_ks max_ks_p mean_ks iter

1 unw 698 298 698 298 0.3925636731 0.2052894288 0.1884194535 . 0.0979184466 .
2 ks.mean.ATE 698 298 655.67541985 228.85005087 0.1197286705 0.0549551078 0.0401275716 . 0.022359505 2603
3 es.mean.ATE 698 298 663.84002267 237.14437956 0.1135056019 0.0539249993 0.0381939155 . 0.0246343475 1303

Because the outcome is dichotomous, we use the PROC SURVEYFREQ procedure to obtain a weighted proportions and test for differences. The chisq option requests the Rao-Scott chi-square test and the row option requests the row percentages.

proc surveyfreq data=_inputds;
tables sixMonthSurvive * abcix / chisq col;
weights es_mean_ate;

SAS Output: Weighted Frequencies and Chi-Square Test

                                              07:07 Tuesday, January 7, 2014   6

The SURVEYFREQ Procedure

Data Summary

Number of Observations 996
Sum of Weights 1838.76433

Table of sixMonthSurvive by abcix

Month Weighted Std Dev of Std Err of
Survive abcix Frequency Frequency Wgt Freq Percent Percent
FALSE 15 47.93424 14.02204 2.6069 0.7530
11 14.24984 4.34049 0.7750 0.2372

Total 26 62.18409 14.63162 3.3818 0.7853
TRUE 283 823.91801 48.24287 44.8082 1.9149
687 952.66223 21.87682 51.8099 1.8755

Total 970 1777 35.04639 96.6182 0.7853
Total 298 871.85225 49.44297 47.4151 1.8872
698 966.91207 21.68290 52.5849 1.8872

Total 996 1839 34.93242 100.000

Table of sixMonthSurvive by abcix

Month Column Std Err of
Survive abcix Percent Col Percent
FALSE 5.4980 1.5674
1.4737 0.4483

TRUE 94.5020 1.5674
98.5263 0.4483

Total 100.000


The reweighting does not diminish the association between the treatment and the outcome. Indeed, it is marginally more significant after the reweighting.

4. Non-response weights

The twang macros were designed to estimate propensity score weights for the evaluation of treatment effects in observational or quasi-experimental studies. However, we find that the package includes functions and diagnostic tools that are highly valuable for other applications, such as for generating and diagnosing nonresponse weights for survey nonresponse or study attrition. We now present an example that uses the tools in twang. This example uses the subset of the US Sustaining Effects Study data distributed with the HLM software (Bryk, Raudenbush, Congdon, 1996), also available in the R package mlmRev, and included with the twang macros in the SAS data set egsingle.sas7bdat. The data include mathematics test scores for 1721 students in kindergarten to fourth grade. They also include student race (black, Hispanic, or other), gender, an indicator for whether or not the student had been retained in grade, the percent low income students at the school, the school size, the percent of mobile students, the students’ grade-levels, student and school IDs, and grades converted to year by centering. The study analysis plans to analyze growth in math achievement from grade 1 to grade 4 using only students with complete data. However, the students with complete data differ from other students. To reduce bias that could potentially result from excluding incomplete cases, our analysis plan is to weight complete cases with nonresponse weights.

The goal of nonresponse weighting is to develop weights for the respondents that make them look like the entire sample — both the respondents and nonrespondents. Since the respondents already look like themselves, the hard part is to figure out how well each respondent represents the nonrespondents. Nonresponse weights equal the reciprocal of the probability of response and are applied only to respondents.

Note that the probability of response is equivalent to the propensity score if we consider subjects with an observed outcome to be the “treated” group, and those with an unobserved outcome to be the “controls”. We wish to reweight the sample to make it equivalent to the population from which the sample was drawn, so ATE weights are appropriate in this case. Further, recall that the weights for the treated subjects are 1/p in an ATE analysis. Therefore we can reweight the sample of respondents using the weights returned from the %ps macro.

Before we can generate nonresponse weights, we need to prepare the data using the following commands. The data contain zero, one or two observations for students from grade “0” (kindergarten) and zero or one observation for each of grades 1 to 4. Only students with data from each of grades 1 to 4 will be included so we need to identify those students. We also need to collapse the data back to a single record per student and create a race variables with values 1 if “Black”, 2 if “Hispanic” and 3 otherwise.

proc sort data=sasin.egsingle out=egsingle;
by childid grade;

data egsingle;
set egsingle;
by childid;
retain gpatt;
length gpatt $6.;
if black = 1 then race = 1;
else if hispanic = 1 then race = 2;
else race = 3;
if first.childid then gpatt = put(grade, best1.);
else gpatt = compress(gpatt || put(grade, best1.));
if last.childid then do;
resp = index(gpatt, "1234") > 0;

proc freq data=egsingle;
table resp race;

SAS Output: Frequency Counts of Respondents and Racial Codes

                                 The SAS System                                3
08:55 Tuesday, January 7, 2014

The FREQ Procedure

Cumulative Cumulative
resp Frequency Percent Frequency Percent
0 878 51.02 878 51.02
1 843 48.98 1721 100.00

Cumulative Cumulative
race Frequency Percent Frequency Percent
1 1195 69.44 1195 69.44
2 250 14.53 1445 83.96
3 276 16.04 1721 100.00

There are 1721 children in the study and 843 (49%) have the necessary four years of outcome data.

As discussed above, to use %ps to estimate nonresponse, we let respondents be the treatment group by modeling an indicator of response.

vars=race female size lowinc mobility,
stopmethod=es.mean ks.max,
estimand = "ATE",
Rcmd=C:\Program Files\R\R-3.0.2\bin\x64\R.exe,

SAS Output: Balance Table for Nonresponse Analysis

                                                                         Balance table: unw                                        10:13 Tuesday, January 7, 2014   5

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

1 unw.race:1 0.656 0.475 0.731 0.443 -0.158 18.402 0 0.075 0 unw
2 unw.race:2 0.129 0.336 0.161 0.367 -0.093 . . 0.031 0 unw
3 unw.race:3 0.215 0.411 0.108 0.311 0.259 . . 0.107 0 unw
4 unw.female:Female 0.466 0.499 0.517 0.5 -0.102 4.454 0.035 0.051 0.035 unw
5 unw.female:Male 0.534 0.499 0.483 0.5 0.102 . . 0.051 0.035 unw
6 unw.size 750.222 316.351 761.328 312.387 -0.035 -0.733 0.464 0.066 0.043 unw
7 unw.lowinc 75.492 28.581 80.745 24.082 -0.198 -4.116 0 0.1 0 unw
8 unw.mobility 32.662 14.044 36.436 13.701 -0.27 -5.642 0 0.122 0 unw

Balance table: ks.max.ATE 10:13 Tuesday, January 7, 2014 6

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

17 ks.max.ATE.race:1 0.689 0.463 0.704 0.456 -0.032 0.386 0.679 0.015 0.679 ks.max.ATE
18 ks.max.ATE.race:2 0.142 0.349 0.144 0.351 -0.005 . . 0.002 0.679 ks.max.ATE
19 ks.max.ATE.race:3 0.169 0.374 0.152 0.359 0.044 . . 0.017 0.679 ks.max.ATE
20 ks.max.ATE.female:Female 0.487 0.5 0.491 0.5 -0.008 0.024 0.877 0.004 0.877 ks.max.ATE
21 ks.max.ATE.female:Male 0.513 0.5 0.509 0.5 0.008 . . 0.004 0.877 ks.max.ATE
22 ks.max.ATE.size 756.722 313.079 758.894 314.197 -0.007 -0.138 0.89 0.024 0.974 ks.max.ATE
23 ks.max.ATE.lowinc 78.517 27.164 78.479 26.479 0.001 0.027 0.979 0.026 0.947 ks.max.ATE
24 ks.max.ATE.mobility 34.227 13.696 34.787 13.972 -0.04 -0.791 0.429 0.022 0.991 ks.max.ATE

Balance table: es.mean.ATE 10:13 Tuesday, January 7, 2014 7

Obs row_name tx_mn tx_sd ct_mn ct_sd std_eff_sz stat p ks ks_pval table_name

9 es.mean.ATE.race:1 0.689 0.463 0.704 0.457 -0.031 0.39 0.676 0.014 0.676 es.mean.ATE
10 es.mean.ATE.race:2 0.142 0.349 0.144 0.351 -0.006 . . 0.002 0.676 es.mean.ATE
11 es.mean.ATE.race:3 0.169 0.374 0.152 0.359 0.045 . . 0.017 0.676 es.mean.ATE
12 es.mean.ATE.female:Female 0.487 0.5 0.491 0.5 -0.007 0.017 0.896 0.003 0.896 es.mean.ATE
13 es.mean.ATE.female:Male 0.513 0.5 0.509 0.5 0.007 . . 0.003 0.896 es.mean.ATE
14 es.mean.ATE.size 756.727 313.071 758.829 314.231 -0.007 -0.133 0.894 0.024 0.971 es.mean.ATE
15 es.mean.ATE.lowinc 78.518 27.158 78.467 26.482 0.002 0.037 0.971 0.026 0.94 es.mean.ATE
16 es.mean.ATE.mobility 34.226 13.697 34.781 13.972 -0.04 -0.784 0.433 0.022 0.99 es.mean.ATE

The final steps are to rename the ATE weights for the “ks.max” stopping rule (renamed nonresp_wt) and include only records with complete information in a permanent SAS data set (sasin.egsingle_nonrespwt) that can be used for analyses.

proc sort data=_inputds out=_inputds;
by childid;

proc sort data=sasin.egsingle out=egsingle;
by childid;

data egsingle_nrespwt;
merge _inputds(keep=childid ks_max_ate resp) egsingle;
by childid;
if resp = 1;

proc sort data=egsingle_nrespwt; by childid grade;

The details of twang

Propensity scores and weighting

Propensity scores can be used to reweight comparison cases so that the distribution of their features match the distribution of features of the treatment cases, for ATT, or cases from both treatment and control groups to match each other, for ATE (Rosenbaum 1987, Wooldridge 2002, Hirano and Imbens 2001, McCaffrey et al. 2004) Let f(xt = 1) be the distribution of features for the treatment cases and f(xt = 0) be the distribution of features for the comparison cases. If treatments were randomized then we would expect these two distributions to be similar. When they differ for ATT we will construct a weight, w(x), so that

f(xt = 1) = w(x)f(xt = 0).

For example, if f(age=65, sex=F∣t = 1) = 0.10 and f(age=65, sex=F∣t = 0) = 0.05 (i.e. 10% of the treatment cases and 5% of the comparison cases are 65 year old females) then we need to give a weight of 2.0 to every 65 year old female in the comparison group so that they have the same representation as in the treatment group. More generally, we can solve Equation 3, below, for w(x) and apply Bayes Theorem to the numerator and the denominator to give an expression for the propensity score weight for comparison cases,

equation three

where K is a normalization constant that will cancel out in the outcomes analysis. Equation ([eq:weight]) indicates that if we assign a weight to comparison case i equal to the odds that a case with features xi would be exposed to the treatment, then the distribution of their features would balance. Note that for comparison cases with features that are atypical of treatment cases, the propensity score P(t = 1∣x) would be near 0 and would produce a weight near 0. On the other hand, comparison cases with features typical of the treatment cases would receive larger weights.

For ATE, each group is weighted to match the population. The weight must satisfy:

f(xt = 1) = w(x)f(x), and

f(xt = 0) = w(x)f(x), and

Again using Bayes Theorem we obtain w(x) = 1/f(xt = 1) for the treatment group and w(x) = 1/f(xt = 0) for the control group.

5.2. Estimating the propensity score

In randomized studies P(t = 1∣x) is known and fixed in the study design. In observational studies the propensity score is unknown and must be estimated, but poor estimation of the propensity scores can cause just as much of a problem for estimating treatment effects as poor regression modeling of the outcome. Linear logistic regression is the common method for estimating propensity scores, and can suffice for many problems. Linear logistic regression for propensity scores estimates the log-odds of a case being in the treatment given xas

equation six

Usually, β is selected to maximize the logistic log-likelihood

equation seven

Maximizing the above provides the maximum likelihood estimates of β. However, in an attempt to remove as much confounding as possible, observational studies often record data on a large number of potential confounders, many of which can be correlated with one another. Standard methods for fitting logistic regression models to such data with the iteratively reweighted least squares algorithm can be statistically and numerically unstable. To improve the propensity score estimates we might also wish to include non-linear effects and interactions in x. The inclusion of such terms only increases the instability of the models.

One increasingly popular method for fitting models with numerous correlated variables is the lasso (least absolute subset selection and shrinkage operator) introduced in statistics in Tibshirani (1996). For logistic regression, lasso estimation replaces ([eq:loglikelihood]) with a version that penalizes the absolute magnitude of the coefficients

equation eight

The second term on the right-hand side of the equation is the penalty term since it decreases the overall of ℓβ when there are coefficients that are large in absolute value. Setting λ = 0 returns the standard (and potentially unstable) logistic regression estimates of β. Setting λ to be very large essentially forces all of the βj to be equal to 0 (the penalty excludes β0). For a fixed value of λ the estimated $\hat\beta$ can have many coefficients exactly equal to 0, not just extremely small but precisely 0, and only the most powerful predictors of t will be non-zero. As a result the absolute penalty operates as a variable selection penalty. In practice, if we have several predictors of t that are highly correlated with each other, the lasso tends to include all of them in the model, shrink their coefficients toward 0, and produce a predictive model that utilizes all of the information in the covariates, producing a model with greater out-of-sample predictive performance than models fit using variable subset selection methods.

Our aim is to include as covariates all piecewise constant functions of the potential confounders and their interactions. That is, in x we will include indicator functions for continuous variables like I(age < 15), I(age < 16), …, I(age < 90), etc., for categorical variables like I(sex = male), I(prior MI = TRUE), and interactions among them like I(age < 16)I(sex = male)I(prior MI = TRUE). This collection of basis functions spans a plausible set of propensity score functions, are computationally efficient, and are flat at the extremes of x reducing the likelihood of propensity score estimates near 0 and 1 that can occur with linear basis functions of x. Theoretically with the lasso we can estimate the model in ([eq:lasso]), selecting a λ small enough so that it will eliminate most of the irrelevant terms and yield a sparse model with only the most important main effects and interactions. Boosting (Friedman 2001, 2003, Ridgeway 1999) effectively implements this strategy using a computationally efficient method that Efron et al. (2004) showed is equivalent to optimizing ([eq:lasso]). With boosting it is possible to maximize ([eq:lasso]) for a range of values of λ with no additional computational effort than for a specific value of λ. We use boosted logistic regression as implemented in the generalized boosted modeling (gbm) package in R (Ridgeway 2005).

Evaluating the weights

As with regression analyses, propensity score methods cannot adjust for unmeasured covariates that are uncorrelated with the observed covariates. Nonetheless, the quality of the adjustment for the observed covariates achieved by propensity score weighting is easy to evaluate. The estimated propensity score weights should equalize the distributions of the cases’ features as in ([eq:balance]). This implies that weighted statistics of the covariates of the comparison group should equal the same statistics for the treatment group. For example, the weighted average of the age of comparison cases should equal the average age of the treatment cases. To assess the quality of the propensity score weights one could compare a variety of statistics such as means, medians, variances, and Kolmogorov-Smirnov statistics for each covariate as well as interactions. The twang package provides both the standardized effect sizes and KS statistics and p-values testing for differences in the means and distributions of the covariates for analysts to use in assessing balance.

Analysis of outcomes

With propensity score analyses the final outcomes analysis is generally straightforward, while the propensity score estimation may require complex modeling. Once we have weights that equalize the distribution of features of treatment and control cases by reweighting. For ATT, we give each treatment case a weight of 1 and each comparison case a weight wi = p(xi)/(1 − p(xi)). To estimate the ATE, we give control cases weight wi = 1/p(xi) and we give the treatment cases wi = 1/(1 − p(xi). We then estimate the treatment effect estimate with a weighted regression model that contains only a treatment indicator. No additional covariates are needed if the weights account for differences in x.

A combination of propensity score weighting and covariate adjustment can be useful for several reasons. First, the propensity scores may not have been able to completely balance all of the covariates. The inclusion of these covariates in addition to the treatment indicator in a weighted regression model may correct this if the imbalance is relatively small. Second, in addition to exposure, the relationship between some of the covariates and the outcome may also be of interest. Their inclusion can provide coefficients that can estimate the direction and magnitude of the relationship. Third, as with randomized trials, stratifying on covariates that are highly correlated with the outcome can improve the precision of estimates. Lastly, the some treatment effect estimators that utilize an outcomes regression model and propensity scores are "doubly robust" in the sense that if either the propensity score model is correct or the regression model is correct then the treatment effect estimator will be unbiased (Bang & Robins 2005).


[1] Bang H. and J. Robins (2005). "Doubly robust estimation in missing data and causal inference models," Biometrics 61:692–972.

[2] Bland M. (2013). "Do baseline p-values follow a uniform distribution in randomised trials?" PLoS ONE 8(10): e76010: 1–5.

[3] Dehejia, R.H. and S. Wahba (1999). "Causal effects in nonexperimental studies: re-evaluating the evaluation of training programs," Journal of the American Statistical Association 94:1053–1062.

[4] Efron, B., T. Hastie, I. Johnstone, R. Tibshirani (2004). "Least angle regression," Annals of Statistics 32(2):407–499.

[5] Friedman, J.H. (2001). "Greedy function approximation: a gradient boosting machine," Annals of Statistics 29(5):1189–1232.

[6] Friedman, J.H. (2002). "Stochastic gradient boosting," Computational Statistics and Data Analysis 38(4):367–378.

[7] Friedman, J.H., T. Hastie, R. Tibshirani (2000). "Additive logistic regression: a statistical view of boosting," Annals of Statistics 28(2):337–374.

[8] Hastie, T., R. Tibshirani, and J. Friedman (2001). The Elements of Statistical Learning. Springer-Verlag, New York.

[9] Helmreich, J.E., and R.M. Pruzek (2009). "PSAgraphics: An R package to support propensity score analysis," Journal of Statistical Software 29(6):1–23.

[10] Hirano, K. and G. Imbens (2001). "Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization," Health Services and Outcomes Research Methodology 2:259–278.

[11] Huppler-Hullsiek, K. and T. Louis (2002) "Propensity score modeling strategies for the causal analysis of observational data," Biostatistics 3:179–193.

[12] Lalonde, R. (1986). "Evaluating the econometric evaluations of training programs with experimental data," American Economic Review 76:604–620.

[13] Little, R. J. and S. Vartivarian (2004). "Does weighting for nonresponse increase the variance of survey means?" ASA Proceedings of the Joint Statistical Meetings, 3897-3904 American Statistical Association (Alexandria, VA)

[14] McCaffrey, D., G. Ridgeway, A. Morral (2004). "Propensity score estimation with boosted regression for evaluating adolescent substance abuse treatment," Psychological Methods 9(4):403–425.

[15] Obenchain, B. (2011). USPS 1.2 package manual.

[16] R Core Team (2013). . R Foundation for Statistical Computing, Vienna, Austria.

[17] Ridgeway, G. (1999). "The state of boosting," Computing Science and Statistics 31:172–181.

[18] Ridgeway, G. (2005). GBM 1.5 package manual.

[19] Ridgeway, G. (2006). "Assessing the effect of race bias in post-traffic stop outcomes using propensity scores." Journal of Quantitative Criminology 22(1):1–29.

[20] Rosenbaum, P. and D. Rubin (1983). "The Central Role of the Propensity Score in Observational Studies for Causal Effects," Biometrika 70(1):41–55.

[21] Rosenbaum, P. (1987). "Model-based direct adjustment," Journal of the American Statistical Association 82:387–394.

[22] Tibshirani, R. (1996). "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society, Series B 58(1):267–288.

[23] Wooldridge, J. (2002). Econometric analysis of cross section and panel data, MIT Press, Cambridge.


  1. The file "" file — available on the Downloads page — contains all the code from this tutorial in text file. Reader can use that file to copy the code and run the examples. (Return to text)

  2. Users might want to add the directory for R to their PATH environmental variable. Doing so would simplify the use of the TWANG macros because the Rcmd can be set to "R" rather than the full path to the executable. To add the R directory to the PATH the user can open a command (cmd) window and type:
    setx PATH "%PATH%;C:\\Program FilesR\R-3.0.2\bin\x64
    where "C:\\Program FilesR\R-3.0.2\bin\x64" should be replaced by actual directory where R is stored. The R directory can be added to the PATH by

    1. Click Start

    2. Right click Computer

    3. Select Properties

    4. Select Advanced system settings

    5. Select Environmental Variable...

    6. In the upper window double click PATH

    7. Add ";C:\\Program FilesR\R-3.0.2\bin\x64" to the end

    8. Click OK until no longer prompted and close out windows that were opened.

    Again ‘C:\\Program FilesR\R-3.0.2\bin\x64'' should be replaced by actual directory where R is stored. (Return to text)

  3. See footnote 2, above, for details on adding the directory to the PATH (Return to text)

  4. The value of should specify the user's version of R. (Return to text)