Identification of Average Treatment Effects in Nonparametric Panel Models

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.

@article{athey2025_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 }
}