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Identification of Average Treatment Effects in Nonparametric Panel Models

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arxiv 2503.19873 v1 pith:6DDRWL5U submitted 2025-03-25 econ.EM cs.LGstat.ME

Susan Athey , Guido Imbens This is my paper
classification econ.EMcs.LGstat.ME
keywords identificationtreatmentaverageeffectsconsistentestimatornonparametricpanel
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This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for example in problems of decompositions of group-level differences in outcomes, such as the much-studied gender wage gap.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Panel Quantile Regression with Common Shocks

    econ.EM 2026-02 unverdicted novelty 7.0 of 10

    Standard FEQR estimator is asymptotically normal under (log N)^2/T to 0 with common shocks present, and a simple covariance estimator is consistent regardless of the shock structure.

  2. Semiparametric inference on identification sets in choice modeling

    math.ST 2026-07 accept novelty 6.0 of 10

    New inference theory for the identified interval of counterfactual choice probabilities when the mixing distribution is unrestricted: LP duality, von Mises expansions, and an EM-based membership certificate.

  3. Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

    stat.ML 2025-05 conditional novelty 6.0 of 10

    CoDEAL imputes missing counterfactuals in staggered panel data using DNN covariate adjustment and multi-output autoencoder factors, then estimates unit-specific treatment effects.

  4. Shrinkage-Based Regressions with Many Related Treatments

    econ.EM 2025-07 conditional novelty 4.0 of 10

    A customized ridge regression with an unpenalized focal treatment effect produces lower-variance estimates for many sparse sub-treatments and exactly recovers the single-treatment estimator.

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