Affiliation:
1. Zhejiang University, Zhejiang, China
2. Tsinghua University, Beijing, China
3. NetEase Fuxi AI Lab, Zhejiang, China
Abstract
Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it’s an art rather than science to find valid IVs in many real-world scenes. Moreover, the predefined hand-made IVs could be weak or erroneous by violating the conditions of valid IVs. These thorny facts hinder the application of the IV-based counterfactual prediction methods. In this article, we propose a novel Automatic Instrumental Variable decomposition (AutoIV) algorithm to automatically generate representations serving the role of IVs from observed variables (IV candidates). Specifically, we let the learned IV representations satisfy the relevance condition with the treatment and exclusion condition with the outcome via mutual information maximization and minimization constraints, respectively. We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome. The IV and confounder representations compete for the information with their constraints in an adversarial game, which allows us to get valid IV representations for IV-based counterfactual prediction. Extensive experiments demonstrate that our method generates valid IV representations for accurate IV-based counterfactual prediction.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Key R & D Projects of the Ministry of Science and Technology
Fundamental Research Funds for the Central Universities and Zhejiang Province Natural Science Foundation
Tsinghua University Initiative Scientific Research Grant
Technology and Innovation Major Project of the Ministry of Science and Technology of China
Publisher
Association for Computing Machinery (ACM)
Cited by
15 articles.
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