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ANC Seminar: Mihaela van Der Schaar

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Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms

What
  • ANC/DTC Seminar
When Sep 26, 2018
from 04:00 PM to 05:00 PM
Where IF 4.31/4.33
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We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. We first develop a theoretical foundation of causal inference for individualized treatment effects based on information theory. Next, we use this theory, to construct an information-optimal Bayesian causal inference algorithm.  This algorithm embeds the potential outcomes in a vector-valued reproducing kernel Hilbert space and uses a multi-task Gaussian process prior over that space to infer the individualized causal effects. We show that our model significantly outperforms the state-of-the-art causal inference models. The talk will conclude with a discussion of the impact of this work on precision medicine and clinical trials.