This article reviews and comments on three major expansions of propensity score methods in recent decades. First, how to use generalized propensity scores to tackle multi-categorical or continuous treatment variables is shown in procedures of propensity score regression adjustment and propensity score weighting. Second, the counterfactual framework of causal inference in the analysis of mediation mechanisms is reviewed and the decomposition of the causal relationship between variables into causal direct effects and causal indirect effects is illustrated. Third, the heterogeneous treatment effect across the distribution of propensity score values is discussed in the framework of the stratification-multilevel model. For each methodological breakthrough, this article comments on potential issues which deserve serious attention in the practical application of these methods.
Language: en
LA - en SN - 0011-3921 UR - http://dx.doi.org/10.1177/0011392115589599 ID - ref1 ER -