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Casual Inference using Generalized Empirical Likelihood Methods

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Listed:
  • Pierre Chausse

    (Department of Economics, University of Waterloo)

  • George Luta

    (Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University)

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).

Suggested Citation

  • Pierre Chausse & George Luta, 2017. "Casual Inference using Generalized Empirical Likelihood Methods," Working Papers 1707, University of Waterloo, Department of Economics, revised Dec 2017.
    • Handle: RePEc:wat:wpaper:1707
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

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