arXiv 21 Oct 2024 · Econometrics
arXiv:2410.15634 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variables (IV) estimation is a fundamental method in econometrics and statistics for estimating causal effects in the presence of unobserved confounding. However, challenges such as untestable model assumptions and poor finite sample properties have undermined its reliability in practice. Viewing common issues in IV estimation as distributional uncertainties, we propose DRIVE, a distributionally robust IV estimation method. We show that DRIVE minimizes a square root variant of ridge regularized two stage least squares (TSLS) objective when the ambiguity set is based on a Wasserstein distance. In addition, we develop a novel asymptotic theory for this estimator, showing that it achieves consistency without requiring the regularization parameter to vanish. This novel property ensures that the estimator is robust to distributional uncertainties that persist in large samples. We further derive the asymptotic distribution of Wasserstein DRIVE and propose data-driven procedures to select the regularization parameter based on theoretical results. Simulation studies demonstrate the superior finite sample performance of Wasserstein DRIVE in terms of estimation error and out-of-sample prediction. Due to its regularization and robustness properties, Wasserstein DRIVE presents an appealing option when the practitioner is uncertain about model assumptions or distributional shifts in data.
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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 | Blanchet, J., Kang, Y., and Murthy, K (2019) Robust wasserstein profile inference and applications to machine learning | 0.888 | 10 | 5 | 70% |
| 2 | Gao, R. and Kleywegt, A (2023) Distributionally robust stochastic optimization with wasserstein distance | 0.843 | 5 | 4 | 60% |
| 3 | Andrews, I., Stock, J. H., and Sun, L (2019) Weak instruments in instrumental variables regression: Theory and practice | 0.737 | 4 | 4 | 50% |
| 4 | Young, A (2022) Consistency without inference: Instrumental variables in practical application | 0.737 | 4 | 3 | 50% |
| 5 | Adjaho, C. and Christensen, T (2022) Externally valid treatment choice | 0.737 | 3 | 3 | 67% |
| 6 | Anderson, T. W. and Rubin, H (1949) Estimation of the parameters of a single equation in a complete system of stochastic equations | 0.737 | 3 | 3 | 67% |
| 7 | Fu, W. and Knight, K (2000) Asymptotics for lasso-type estimators | 0.737 | 3 | 3 | 67% |
| 8 | Murray, M. P (2006) Avoiding invalid instruments and coping with weak instruments | 0.737 | 3 | 3 | 67% |
| 9 | Imbens, G. W. and Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences | 0.737 | 3 | 2 | 100% |
| 10 | Rothenhäusler, D., Meinshausen, N., Bühlmann, P., Peters, J., et al (2021) Anchor regression: Heterogeneous data meet causality | 0.648 | 11 | 4 | 27% |
Showing the top 10 of 140 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distributionally Robust Treatment Effect | 0.405 | 1 | 1 |
| 2 | An econometrician's guide to optimal transport | 0.405 | 1 | 1 |
| 3 | IV regression with distribution-valued outcomes | 0.405 | 1 | 1 |