Propensity Score Matching - Overview

Overview

PSM is for cases of causal inference and simple selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment unit is difficult because units must be compared across a high-dimensional set of pretreatment characteristics.

In normal Matching we match on single characteristics that distinguish treatment and control groups (to try to make them more alike). But If the two groups do not have substantial overlap, then substantial error may be introduced: E.g., if only the worst cases from the untreated “comparison” group are compared to only the best cases from the treatment group, the result may be regression toward the mean which may make the comparison group look better or worse than reality.

PSM employs a predicted probability of group membership e.g., treatment vs. control group—based on observed predictors, usually obtained from logistic regression to create a counterfactual group. Also propensity scores may be used for matching or as covariates—alone or with other matching variables or covariates.