Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah
arXiv 18 Feb 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2402.11652 · PDF · DOI · OpenAlex · Extracted main text
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-sample and asymptotic guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate. Simulation results demonstrate the relevance of the formal properties of the estimators analyzed in this article.
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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 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 4 | 1 | 100% |
| 2 | Agarwal, A., Dahleh, M., Shah, D., and Shen, D (2023) Causal matrix completion self | 0.585 | 3 | 1 | 100% |
| 3 | Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2021) Matrix completion methods for causal panel data models | 0.585 | 3 | 1 | 100% |
| 4 | Dwivedi, R., Tian, K., Tomkins, S., Klasnja, P., Murphy, S., and Sha… (2022) Counterfactual inference for sequential experiments self | 0.585 | 3 | 1 | 100% |
| 5 | Xiong, R. and Pelger, M (2023) Large dimensional latent factor modeling with missing observations and applications to causal inference | 0.585 | 3 | 1 | 100% |
| 6 | Bai, J. and Ng, S (2021) Matrix completion, counterfactuals, and factor analysis of missing data | 0.527 | 25 | 1 | 44% |
| 7 | Abadie, A. and Imbens, G. W (2006) Large sample properties of matching estimators for average treatment effects self | 0.511 | 2 | 1 | 100% |
| 8 | Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., and Wa… (2021) Synthetic difference-in-differences | 0.511 | 2 | 1 | 100% |
| 9 | Nguyen, L. T., Kim, J., and Shim, B (2019) Low-rank matrix completion: A contemporary survey | 0.511 | 2 | 1 | 100% |
| 10 | Agarwal, A., Shah, D., and Shen, D (2023) Synthetic interventions self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities | 1.000 | 21 | 3 |
| 2 | Causal Models for Longitudinal and Panel Data: A Survey | 0.405 | 1 | 1 |
| 3 | An Introduction to Double/Debiased Machine Learning | 0.405 | 1 | 1 |
| 4 | Using Multiple Outcomes to Adjust Standard Errors for Spatial Correlation | 0.405 | 1 | 1 |