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Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models

Author

Listed:
  • Laurent Davezies

    (CREST-ENSAE)

  • Xavier D’Haultfoeuille

    (CREST-ENSAE, PSE)

  • Louise Laage

    (Georgetown University)

Abstract

This paper studies identification and estimation of average causal effects, such as average marginal or treatment effects, in fixed effects logit models with short panels. Relating the identified set of these effects to an extremal moment problem, we first show how to obtain sharp bounds on such effects simply, without any optimization. We also consider even simpler outer bounds, which, contrary to the sharp bounds, do not require any first-step nonparametric estimators. We build confidence intervals based on these two approaches and show their asymptotic validity. Monte Carlo simulations suggest that both approaches work well in practice, the second being typically competitive in terms of interval length. Finally, we show that our method is also useful to measure treatment effect heterogeneity.

Suggested Citation

  • Laurent Davezies & Xavier D’Haultfoeuille & Louise Laage, 2025. "Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models," Working Papers 2025-02, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2025-02
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    More about this item

    Keywords

    Fixed effects logit models; panel data; partial identification.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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