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

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

A Tutorial for the Selection Bias Decomposition (SBdecomp) Package

Published Dec 29, 2020

by Layla Parast, Beth Ann Griffin

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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.

The SBdecomp package quantifies the proportion of the estimated selection bias explained by each confounder when estimating causal effects using propensity score weights. The authors propose two approaches to quantify the proportion of the selection bias explained by each observed confounder—a single confounder removal approach and a single confounder inclusion approach.

This tool will help analyze data when there is a substantive interest in identifying the variable or variables that explains the largest proportion of the estimated selection bias.

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This research was funded by the National Institute on Drug Abuse and conducted by the Social and Behavioral Policy Program within RAND Social and Economic Well-Being.

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