Paper Info
Title
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Abstract
AbstractInstrumental 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.
Year
DOI
Venue
2022
10.1145/3494568
ACM Transactions on Knowledge Discovery from Data
Keywords
DocType
Volume
Instrumental variable, counterfactual prediction, causal inference, representation learning, mutual information
Journal
16
Issue
ISSN
Citations 
4
1556-4681
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Junkun Yuan100.68
Anpeng Wu200.34
Kun Kuang300.68
Bo Li401.01
Runze Wu513.05
Fei Wu62209153.88
Lanfen Lin77824.70