Crowdfunding

20722 WordsOct 25, 201483 Pages
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING Marco Caliendo IZA, Bonn Sabine Kopeinig University of Cologne Abstract. 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…show more content…
It originated from the statistical literature and shows a close link to the experimental context.1 Its basic idea is to find in a large group of nonparticipants those individuals who are similar to the participants in all relevant pretreatment characteristics X. That being done, differences in outcomes of this well selected and thus adequate control group and of participants can be attributed to the programme. The underlying identifying assumption is known as unconfoundedness, selection on observables or conditional independence. It should be clear that matching is no ‘magic bullet’ that will solve the evaluation problem in any case. It should only be applied if the underlying identifying assumption can be credibly invoked based on the informational richness of the data and a detailed understanding of the institutional set-up by which selection into treatment takes place (see for example the discussion in Blundell et al., 2005). For the rest of the paper we will assume that this assumption holds. Since conditioning on all relevant covariates is limited in the case of a high dimensional vector X (‘curse of dimensionality’), Rosenbaum and Rubin (1983b) suggest the use of so-called balancing scores b(X), i.e. functions of the relevant observed covariates X such that the conditional distribution of X given b(X) is independent of assignment into treatment. One possible balancing score is the propensity score, i.e. the probability of participating in a programme
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