Identification of Average Treatment Effects in Nonparametric Panel Models
Econometrics
2025-03-26 v1 Machine Learning
Methodology
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.
Cite
@article{arxiv.2503.19873,
title = {Identification of Average Treatment Effects in Nonparametric Panel Models},
author = {Susan Athey and Guido Imbens},
journal= {arXiv preprint arXiv:2503.19873},
year = {2025}
}