Andrew Bennett, Nathan Kallus, Tobias Schnabel
arXiv 29 May 2019 · Statistics — Machine Learning · 31 citations (OpenAlex)
arXiv:1905.12495 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded when the causal effects are complex, the instruments are high-dimensional, and/or the treatment is high-dimensional. In this paper, we propose the DeepGMM algorithm to overcome this. Our algorithm is based on a new variational reformulation of GMM with optimal inverse-covariance weighting that allows us to efficiently control very many moment conditions. We further develop practical techniques for optimization and model selection that make it particularly successful in practice. Our algorithm is also computationally tractable and can handle large-scale datasets. Numerical results show our algorithm matches the performance of the best tuned methods in standard settings and continues to work in high-dimensional settings where even recent methods break.
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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 | G. Lewis and V. Syrgkanis (2018) Adversarial generalized method of moments | 1.000 | 6 | 4 | 100% |
| 2 | J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy (2017) Deep iv: A flexible approach for counterfactual prediction | 0.843 | 3 | 3 | 100% |
| 3 | J. D. Angrist and J.-S. Pischke (2008) Mostly Harmless Econometrics: An Empiricist's Companion | 0.737 | 3 | 2 | 100% |
| 4 | C. Daskalakis, A. Ilyas, V. Syrgkanis, and H. Zeng (2017) Training gans with optimism | 0.644 | 2 | 2 | 100% |
| 5 | L. P. Hansen (1982) Large sample properties of generalized method of moments estimators | 0.644 | 2 | 2 | 100% |
| 6 | L. P. Hansen, J. Heaton, and A. Yaron (1996) Finite-sample properties of some alternative gmm estimators | 0.644 | 2 | 2 | 100% |
| 7 | J. A. Cole, H. Norman, L. B. Weatherby, and A. M. Walker (2006) Drug copayment and adherence in chronic heart failure: effect on cost and outcomes | 0.511 | 2 | 1 | 100% |
| 8 | C. Ai and X. Chen (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.405 | 1 | 1 | 100% |
| 9 | J. D. Angrist and A. B. Krueger (2001) Instrumental variables and the search for identification: From supply and demand to natural experiments | 0.405 | 1 | 1 | 100% |
| 10 | M. Arjovsky, S. Chintala, and L. Bottou (2017) Wasserstein gan | 0.405 | 1 | 1 | 100% |
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