Propensity Score Matching Methods for Non-Experimental Causal Studies

34 Pages Posted: 13 Jul 2000 Last revised: 31 Aug 2022

See all articles by Rajeev H. Dehejia

Rajeev H. Dehejia

New York University (NYU) - Robert F. Wagner Graduate School of Public Service; National Bureau of Economic Research (NBER); Institute for the Study of Labor (IZA); CESifo

Sadek Wahba

Morgan Stanley

Multiple version iconThere are 2 versions of this paper

Date Written: December 1998

Abstract

This paper considers causal inference and sample 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 units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We propose the use of propensity score matching methods and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units from the CPS and PSID and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. We show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.

Suggested Citation

Dehejia, Rajeev H. and Wahba, Sadek, Propensity Score Matching Methods for Non-Experimental Causal Studies (December 1998). NBER Working Paper No. w6829, Available at SSRN: https://ssrn.com/abstract=227608

Rajeev H. Dehejia (Contact Author)

New York University (NYU) - Robert F. Wagner Graduate School of Public Service ( email )

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Institute for the Study of Labor (IZA) ( email )

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CESifo ( email )

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Sadek Wahba

Morgan Stanley ( email )

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