# Identification of Average Treatment Effects in Nonparametric Panel Models

arXiv: https://arxiv.org/abs/2503.19873 · HTML: https://mlsanity.com/paper/2503.19873/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Synthetic Difference in Differences](https://mlsanity.com/paper/1812.09970/) (2018)
- [Estimating network edge probabilities by neighborhood smoothing](https://mlsanity.com/paper/1509.08588/) (2015)
- [What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature](https://mlsanity.com/paper/2201.01194/) (2022)
- [A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data](https://mlsanity.com/paper/2107.00856/) (2021)
- [Optimal Estimation of Large-Dimensional Nonlinear Factor Models](https://mlsanity.com/paper/2311.07243/) (2023)
- [Blocked Clusterwise Regression](https://mlsanity.com/paper/2001.11130/) (2020)
- [Causal Inference in Possibly Nonlinear Factor Models](https://mlsanity.com/paper/2008.13651/) (2020)

## Built on by (most cited first, 2 of 2)

- [Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models](https://mlsanity.com/paper/2505.20536/) (2025)
- [Shrinkage-Based Regressions with Many Related Treatments](https://mlsanity.com/paper/2507.01202/) (2025)

## Abstract (arXiv)

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.