RAND Research Topic: Propensity Score

Toolkit for Weighting and Analysis of Nonequivalent Groups

2021-05-14T04:30:00Z

This tutorial demonstrates the use of the Toolkit for Weighting and Analysis of Nonequivalent Groups Shiny application for time-varying treatments -- for cases when the time-varying treatment or exposure is binary -- using an illustrative example.

Brief Tutorial for Twangcontinuous

2021-03-10T05:45:00Z

This document provides a brief tutorial on using the twangContinous package to estimate causal effects for continuous exposure variables using generalized propensity scores estimated via generalized boosted models.

Quantifying the Bias Due to Observed Individual Confounders in Causal Treatment Effect Estimates

2020-12-29T08:00:00Z

In this tool, the authors explain the methodology behind the primary function of the selection bias decomposition (SBdecomp) package; describe its features, syntax and how to implement the function; and illustrate its use with an example.

Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects

2019-09-19T07:45:00Z

This paper introduces a new approach to estimating the propensity score using Gaussian processes and optimizing hyperparameters with respect to covariate balance.

Optimizing Variance-Bias Trade-Off in the TWANG Package for Estimation of Propensity Scores

2017-12-28T06:00:00Z

This discussion highlights potentially meaningful ways to optimize propensity score machine learning methods to allow for minimal bias and less variability.

Addressing Geographic Confounding Through Spatial Propensity Scores

2017-12-14T06:00:00Z

A spatial "doubly robust" estimator can minimize geographically related risk difference among racial/ethnic groups in health disparities studies.

Propensity Scores for Repeated Treatments

2017-09-14T05:00:00Z

This tutorial describes the use of the TWANG package in R to estimate inverse probability of treatment weights (IPTWs) when one has time varying treatments or sequences of treatments over time.

A Propensity-Score-Weighted Population-Based Study of the Health Benefits of Dogs and Cats for Children

2017-08-29T08:30:00Z

Counter to widely held beliefs, pet ownership does not improve children's general or psychological health.

Propensity Scores for Multiple Treatments

2017-05-05T06:15:00Z

This tutorial explains the syntax and features related to the implementation of the MNPS commands in the Stata TWANG series.

Propensity Scores for Multiple Treatments

2015-03-13T10:00:00Z

This tutorial explains the syntax and features related to the implementation of the MNPS function in the SAS TWANG macros.

A Tutorial on Propensity Score Estimation for Multiple Treatments Using Generalized Boosted Models

2013-01-01T09:00:00Z

The use of propensity scores to control for pretreatment imbalances on observed variables in non-randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade.

Bias and Variance Trade-Offs When Combining Propensity Score Weighting and Regression

2012-06-01T09:00:00Z

There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance.

Assessing the Effect of Race Bias in Post-Traffic Stop Outcomes Using Propensity Scores

2005-12-31T21:00:00Z

Due to community demands, case settlements, and state laws concerning racial profiling, police departments are collecting data on traffic stops.

Assessing the Effect of Race Bias in Post-traffic Stop Outcomes Using Propensity Scores

2005-12-31T21:00:00Z

Addresses the role race plays a role in officers' use of discretion in traffic stops by proposing a technique to determine the extent to which race bias affects citation rates, search rates, and the duration of the stop.

Propensity Score Estimation with Boosted Regression for Evaluating Causal Effects in Observational Studies

2003-12-31T21:00:00Z

Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.