Abstract and Keywords
The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate matching techniques and several propensity score methods, like propensity score matching, subclassification, inverse-propensity weighting, and regression estimation. In addition to the theoretical aspects, we give practical guidelines for implementing these techniques and discuss the conditions under which these techniques warrant a causal interpretation of the estimated treatment effect. In particular, we emphasize that the selection of covariates and their reliable measurement is more important than the choice of a specific matching strategy.
Keywords: Matching, propensity scores, observational study, Rubin Causal Model, potential outcomes, propensity score subclassification, inverse-propensity weighting, propensity score regression estimation, sensitivity analyses
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