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Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs

Rajeev H. Dehejia and Sadek Wahba
Journal of the American Statistical Association
Vol. 94, No. 448 (Dec., 1999), pp. 1053-1062
DOI: 10.2307/2669919
Stable URL: http://www.jstor.org/stable/2669919
Page Count: 10
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Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs
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Abstract

This article uses propensity score methods to estimate the treatment impact of the National Supported Work (NSW) Demonstration, a labor training program, on postintervention earnings. We use data from Lalonde's evaluation of nonexperimental methods that combine the treated units from a randomized evaluation of the NSW with nonexperimental comparison units drawn from survey datasets. We apply propensity score methods to this composite dataset and demonstrate that, relative to the estimators that Lalonde evaluates, propensity score estimates of the treatment impact are much closer to the experimental benchmark estimate. Propensity score methods assume that the variables associated with assignment to treatment are observed (referred to as ignorable treatment assignment, or selection on observables). Even under this assumption, it is difficult to control for differences between the treatment and comparison groups when they are dissimilar and when there are many preintervention variables. The estimated propensity score (the probability of assignment to treatment, conditional on preintervention variables) summarizes the preintervention variables. This offers a diagnostic on the comparability of the treatment and comparison groups, because one has only to compare the estimated propensity score across the two groups. We discuss several methods (such as stratification and matching) that use the propensity score to estimate the treatment impact. When the range of estimated propensity scores of the treatment and comparison groups overlap, these methods can estimate the treatment impact for the treatment group. A sensitivity analysis shows that our estimates are not sensitive to the specification of the estimated propensity score, but are sensitive to the assumption of selection on observables. We conclude that when the treatment and comparison groups overlap, and when the variables determining assignment to treatment are observed, these methods provide a means to estimate the treatment impact. Even though propensity score methods are not always applicable, they offer a diagnostic on the quality of nonexperimental comparison groups in terms of observable preintervention variables.

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