Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
arXiv 26 Mar 2025 · Econometrics
arXiv:2503.20769 · PDF · DOI · OpenAlex · Extracted main text
The presence of unobserved confounders is one of the main challenges in identifying treatment effects. In this paper, we propose a new approach to causal inference using panel data with large large $N$ and $T$. Our approach imputes the untreated potential outcomes for treated units using the outcomes for untreated individuals with similar values of the latent confounders. In order to find units with similar latent characteristics, we utilize long pre-treatment histories of the outcomes. Our analysis is based on a nonparametric, nonlinear, and nonseparable factor model for untreated potential outcomes and treatments. The model satisfies minimal smoothness requirements. We impute both missing counterfactual outcomes and propensity scores using kernel smoothing based on the constructed measure of latent similarity between units, and demonstrate that our estimates can achieve the optimal nonparametric rate of convergence up to log terms. Using these estimates, we construct a doubly robust estimator of the period-specifc average treatment effect on the treated (ATT), and provide conditions, under which this estimator is $\sqrt{N}$-consistent, and asymptotically normal and unbiased. Our simulation study demonstrates that our method provides accurate inference for a wide range of data generating processes.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Abadie, A., Agarwal, A., Dwivedi, R., and Shah, A (2024) Doubly robust inference in causal latent factor models | 1.000 | 21 | 3 | 100% |
| 2 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning | 1.000 | 9 | 3 | 100% |
| 3 | Fernández-Val, I., Freeman, H., and Weidner, M (2021) Low-rank approximations of nonseparable panel models | 1.000 | 5 | 3 | 100% |
| 4 | Feng, Y (2024) Causal inference in possibly nonlinear factor models | 0.874 | 20 | 2 | 100% |
| 5 | Zhang, Y., Levina, E., and Zhu, J (2017) Estimating network edge probabilities by neighbourhood smoothing | 0.874 | 10 | 2 | 100% |
| 6 | Stone, C. J (1980) Optimal rates of convergence for nonparametric estimators | 0.874 | 7 | 2 | 100% |
| 7 | Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2021) Matrix completion methods for causal panel data models | 0.811 | 4 | 2 | 100% |
| 8 | Robins, J., Li, L., Tchetgen, E., van der Vaart, A., et al (2008) Higher order influence functions and minimax estimation of nonlinear functionals | 0.811 | 4 | 2 | 100% |
| 9 | Sun, L. and Abraham, S (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.811 | 4 | 2 | 100% |
| 10 | Agarwal, A., Dahleh, M., Shah, D., and Shen, D (2021) Causal matrix completion | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference on Linear Regressions with Two-Way Unobserved Heterogeneity | 0.928 | 5 | 3 |
| 2 | Flexible Imputation of Incomplete Network Data | 0.874 | 6 | 2 |
| 3 | Inference after discretizing time-varying unobserved heterogeneity | 0.405 | 1 | 1 |