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Variational Causal Inference
Yulun Wu · Layne Price · Zichen Wang · Vassilis Ioannidis · Rob Barton · George Karypis

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in Causal Machine Learning for Real-World Impact »
Event URL: https://openreview.net/forum?id=YUbpFNrREOd »

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.

Author Information

Yulun Wu (University of California, Berkeley)
Layne Price (Amazon)
Zichen Wang (Amazon)
Vassilis Ioannidis (University of Minnesota, Minneapolis)
Rob Barton (Amazon)
George Karypis (University of Minnesota, Minneapolis)

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