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Identification of Average Treatment Effects in Nonparametric Panel Models

Susan Athey, Guido Imbens

arXiv 25 Mar 2025 · Econometrics

arXiv:2503.19873 · PDF · Extracted main text

Abstract

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.

Citation extraction

43
references
50
in-text mentions
43
distinct cited
4
self-citations
6,247
main-text words

appendix boundary found by appendix_command · 77% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Guido W Imbens and Donald B Rubin (2015) Causal Inference in Statistics, Social, and Biomedical Sciences self0.84333100%
2David J Aldous (1981) Representations for partially exchangeable arrays of random variables0.64422100%
3Francine D Blau and Lawrence M Kahn (2017) The gender wage gap: Extent, trends, and explanations0.64422100%
4Gary Chamberlain (1984) Panel data0.64422100%
5James Lynch (1984) Canonical row-column-exchangeable arrays0.64422100%
6Peter McCullagh (2000) Resampling and exchangeable arrays0.64422100%
7Alberto Abadie and Javier Gardeazabal (2003) The economic costs of conflict: A case study of the basque country0.40511100%
8Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california's tobacco control program0.40511100%
9Alberto Abadie (2019) Using synthetic controls: Feasibility, data requirements, and methodological aspects0.40511100%
10Alberto Abadie, Anish Agarwal, and Devavrat Shah (2023) A causal inference framework for data rich environments0.40511100%

Showing the top 10 of 43 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference after discretizing time-varying unobserved heterogeneity0.64422
2Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models0.64422
3Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities0.51121
4Shrinkage-Based Regressions with Many Related Treatments0.40511
5Panel Quantile Regression with Common Shocks0.40511