Causal effect inference with deep latent-variable models
Name
NIPS-2017-causal-effect-inference-with-deep-latent-variable-models-Paper.pdf
Description
Published version
Size
441.92 KB
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Checksum (MD5)
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Author(s)
Louizos, Christos
Shalit, Uri
Mooij, Joris
Sontag, David Alexander
Zemel, Richard
Welling, Max
Date Issued
December 2017
Journal
Advances in Neural Information Processing Systems
Citation
2017. "Causal effect inference with deep latent-variable models." Advances in Neural Information Processing Systems, 2017-December.
Version
Final published version
Abstract
© 2017 Neural information processing systems foundation. All rights reserved. Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/2017/hash/94b5bde6de888ddf9cde6748ad2523d1-Abstract.html
