Some Practical Guidance for the Implementation of Propensity Score Matching

Propensity Score Matching (PSM) has become a popular approach to estimate causal
treatment effects. It is widely applied when evaluating labour market policies, but empirical
examples can be found in very diverse fields of study. Once the researcher has decided to
use PSM, he is confronted with a lot of questions regarding its implementation. To begin with,
a first decision has to be made concerning the estimation of the propensity score. Following
that one has to decide which matching algorithm to choose and determine the region of
common support. Subsequently, the matching quality has to be assessed and treatment
effects and their standard errors have to be estimated. Furthermore, questions like “what to
do if there is choice-based sampling?” or “when to measure effects?” can be important in
empirical studies. Finally, one might also want to test the sensitivity of estimated treatment
effects with respect to unobserved heterogeneity or failure of the common support condition.
Each implementation step involves a lot of decisions and different approaches can be
thought of. The aim of this paper is to discuss these implementation issues and give some
guidance to researchers who want to use PSM for evaluation purposes.