Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs

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This paper uses propensity score methods to address the question: how well can an observational study estimate the treatment impact of a program? Using data from Lalonde's (1986) influential evaluation of non-experimental methods, we demonstrate that propensity score methods succeed in estimating the treatment impact of the National Supported Work Demonstration. Propensity score methods reduce the task of controlling for differences in pre-intervention variables between the treatment and the non-experimental comparison groups to controlling for differences in the estimated propensity score (the probability of assignment to treatment, conditional on covariates). It is difficult to control for differences in pre-intervention variables when they are numerous and when the treatment and comparison groups are dissimilar, whereas controlling for the estimated propensity score, a single variable on the unit interval, is a straightforward task. We apply several methods, such as stratification on the propensity score and matching on the propensity score, and show that they result in accurate estimates of the treatment impact.

Published: Journal of the American Statistical Association, Vol. 94, no. 448 (December 1999): 1053-1062.

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