Variational Causal Inference

• URL: http://arxiv.org/abs/2209.05935v3
• Date: Tue, 22 Oct 2024 18:45:43 GMT
• Title: Variational Causal Inference
• Authors: Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, Robert A. Barton, George Karypis,
• Abstract summary: Estimating an individual's potential outcomes under counterfactual treatments is a challenging task. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction.
• Score: 29.506997688252294
• License:
• Abstract: Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited. In this case, to construct one's outcome under a counterfactual treatment, it is crucial to leverage individual information contained in its observed factual outcome on top of the covariates. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction under a counterfactual treatment: one source is the individual features embedded in the high-dimensional factual outcome; the other source is the response distribution of similar subjects (subjects with the same covariates) that factually received this treatment of interest.

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