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Markus Gangl

In: The SAGE Handbook of Regression Analysis and Causal Inference

Chapter 12: Matching Estimators for Treatment Effects

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Matching Estimators for Treatment Effects
Matching estimators for treatment effects
MarkusGangl
Introduction

Matching estimators have gained in popularity as flexible tools for estimating treatment effects in observational studies as insights from biometrics, epidemiology and statistics have increasingly spread into the social sciences. Fundamental to all matching estimators is the construction of a control group that is as similar as possible to the treatment group of interest with respect to observed covariates. If observed covariates are sufficient to eliminate the impact of potential confounders of treatment, matching estimators consistently identify and empirically estimate the causal effect of treatment on outcomes. Compared to regression analysis, matching estimators rest on minimal mathematical foundations that are easily accessible to the applied researcher and that result in readily interpretable parameter estimates. Practical implementation ...

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