Some Practical Guidance for the Implementation of Propensity Score Matching

By Marco Caliendo, Sabine Kopeinig. Social Science Research Network, 2008

6,963 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a7f0cc61b3801edd7d3fb41a730ce73e002d7c06

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Citations in the corpus

Gang Membership and Adherence to the “Code of the Street” (2013, methodology): “…was used to calculate the propensity score, which represents the probability of becoming gang involved for the first time at wave 4 given observed characteristics at wave 3.4 The Epanechnikov kernel matching procedure was used to construct the comparison group (Caliendo & Kopeinig, 2008).”
Poverty Reduction Effects of Agricultural Technology Adoption: A Micro-evidence from Rural Tanzania (2012, methodology): “(1998), Caliendo and Kopeinig (2008), and Smith and Todd (2005).”
The microeconometric estimation of treatment effects—An overview (2006, background): “(11)See Rosenbaum (2002) or Caliendo and Kopeinig (2005) for an introduction into propensity score matching estimators and some guidance for their implementation.”
Heterogeneous impact of cooperative membership on farmers’ welfare in Rwanda (2015, background): “First, the common support or overlap condition requires balancing in the covariate distribution between treated and untreated observations to ensure that treatment observations have comparable control observations nearby in the PS distribution (Caliendo and Kopeinig, 2008).”
A Primer for Applying Propensity-Score Matching (2010, methodology): “For example, Heckman, Ichimura and Todd (1998), Lechner (1999), Dehejia and Wahba (2002), and Smith & Todd (2005) use PSM techniques to estimate the impact of labor market and training programs on income; Jalan and Ravallion (2003) evaluate antipoverty workfare programs; Galiani, Gertler and Schargr”
The Causal Effect of Campus Residency on College Student Retention (2011, methodology): “This technique creates a lower variance than nearest neighbor and radius matching, which do not use all available cases, due to maximum use of information (Caliendo & Kopeinig, 2008).”