Auto IV: Counterfactual Prediction via Automatic Instrumental Variable
Decomposition
- URL: http://arxiv.org/abs/2107.05884v1
- Date: Tue, 13 Jul 2021 07:30:21 GMT
- Title: Auto IV: Counterfactual Prediction via Automatic Instrumental Variable
Decomposition
- Authors: Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin
- Abstract summary: Instrumental variables (IVs) play an important role in causal inference with unobserved confounders.
Existing IV-based counterfactual prediction methods need well-predefined IVs.
We propose a novel algorithm to automatically generate representations serving the role of IVs from observed variables.
- Score: 21.90157954233519
- License: http://creativecommons.org/licenses/by/4.0/
- 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 paper, 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.
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