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Poster
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos · Uri Shalit · Joris M Mooij · David Sontag · Richard Zemel · Max Welling

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #188 #None
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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.

Keywords: Generative Models · Causal Inference · Latent Variable Models · Deep Autoencoders

Author Information

Christos Louizos (University of Amsterdam)
Uri Shalit
Joris M Mooij (University of Amsterdam)
David Sontag (MIT)
Richard Zemel (Vector Institute/University of Toronto)
Max Welling (University of Amsterdam and University of California Irvine and CIFAR)

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