Deep Generalized Method of Moments for Instrumental Variable Analysis

Andrew Bennett, Nathan Kallus and Tobias Schnabel

Papers from arXiv.org

Abstract: 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.

Date: 2019-05, Revised 2020-04
New Economics Papers: this item is included in nep-big and nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Advances in Neural Information Processing Systems 32 (2019) 3564--3574

Downloads: (external link)
http://arxiv.org/pdf/1905.12495 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1905.12495

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().