Casual Inference using Generalized Empirical Likelihood Methods

Pierre Chausse and George Luta
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George Luta: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University

No 1707, Working Papers from University of Waterloo, Department of Economics

Abstract: In this paper, we propose a one step method for estimating the average treatment effect, when the assignment to treatment is not random. We use a misspecified generalized empirical likelihood setup in which we constrain the sample to be balanced. We show that the implied probabilities that we obtain play a similar role as the weights from the weighting methods based on the propensity score. In Monte Carlo simulations, we show that GEL dominates many existing methods in terms of bias and root mean squared errors. We then apply our method to the training program studied by Lalonde (1986).

JEL-codes: C13 C21 J01 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2017-12, Revised 2017-12
New Economics Papers: this item is included in nep-ecm and nep-lab
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Persistent link: https://EconPapers.repec.org/RePEc:wat:wpaper:1707

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