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SUMMARY:Causal Inference for Treatment Effects: A Theory and Associated Le
 arning Algorithms - Mihaela van der Schaar (University of Cambridge)
DTSTART:20180315T110000Z
DTEND:20180315T120000Z
UID:TALK102457@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:We investigate the problem of estimating the causal effect of 
 a treatment on individual subjects from observational&nbsp\;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 informa
 tion-optimal Bayesian causal inference algorithm.&nbsp\; This algorithm em
 beds the potential outcomes in a vector-valued reproducing kernel Hilbert 
 space and uses a multi-task Gaussian process prior over that space to infe
 r the individualized causal effects. We show that our algorithm significan
 tly outperforms the state-of-the-art causal inference algorithms. The talk
  will conclude with a discussion of the impact of this work on precision m
 edicine and clinical trials.<br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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