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Poster
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett · Nathan Kallus · Tobias Schnabel

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #183
in Probabilistic Methods -- Causal Inference »

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.

Keywords: Supervised Deep Networks · Causal Inference · Deep Learning · Deep Learning -> Adversarial Networks; Probabilistic Methods
[ Paper]  [ Slides] 

Author Information

Andrew Bennett (Cornell University)
Nathan Kallus (Cornell University)
Tobias Schnabel (Microsoft Research)

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