Alberto Abadie, Anish Agarwal, Devavrat Shah
arXiv 2 Apr 2025 · Econometrics
arXiv:2504.01702 · PDF · Extracted main text
We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of measurements per unit. Our model provides a bridge between the structural causal model view of causal inference common in the graphical models literature with that of the latent factor model view common in the potential outcomes literature. We show how classic models for potential outcomes and treatment assignments fit within our framework. We provide an identification argument for the average treatment effect, the average treatment effect on the treated, and the average treatment effect on the untreated. For any estimator that has a fast enough estimation error rate for a certain nuisance parameter, we establish it is consistent for these various causal parameters. We then show principal component regression is one such estimator that leads to consistent estimation, and we analyze the minimal smoothness required of the potential outcomes function for consistency.
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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 | Agarwal, A. and Singh, R (2021) Causal inference with corrupted data: Measurement error, missing values, discretization, and differential privacy self | 0.693 | 7 | 1 | 100% |
| 2 | Xu, J (2018) Rates of convergence of spectral methods for graphon estimation | 0.644 | 4 | 1 | 100% |
| 3 | Bai, J (2009) Panel data models with interactive fixed effects | 0.585 | 3 | 1 | 100% |
| 4 | Agarwal, A., Shah, D., and Shen, D (2023) Synthetic interventions self | 0.511 | 2 | 1 | 100% |
| 5 | Vershynin, R (2018) High-Dimensional Probability: An Introduction with Applications in Data Science | 0.511 | 2 | 1 | 100% |
| 6 | Agarwal, A. and Singh, R (2021) Causal inference with corrupted data: Measurement error, missing values, discretization, and differential privacy self | 0.405 | 1 | 1 | 100% |
| 7 | Agarwal, A., Dahleh, M., Shah, D., and Shen, D (2023) Causal matrix completion self | 0.405 | 1 | 1 | 100% |
| 8 | Agarwal, A., Shah, D., Shen, D., and Song, D (2019) On robustness of principal component regression self | 0.405 | 1 | 1 | 100% |
| 9 | Agarwal, A., Shah, D., and Shen, D (2020) On model identification and out-of-sample prediction of principal component regression: Applications to synthetic controls self | 0.405 | 1 | 1 | 100% |
| 10 | Agarwal, A., Harris, K., Whitehouse, J., and Wu, Z. S (2023) Adaptive principal component regression with applications to panel data self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification of Average Treatment Effects in Nonparametric Panel Models | 0.405 | 1 | 1 |
| 2 | Synthetic Survival Control: Extending Synthetic Controls for “When-If” Decision | 0.405 | 1 | 1 |
| 3 | On Evolution-Based Models for Experimentation Under Interference | 0.405 | 1 | 1 |