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Activity Number: 389 - Unsupervised Learning with Latent Variables for Biobehavioral Research
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Mental Health Statistics Section
Abstract #309542
Title: Dealing with Latent Variable Confounding Using Auxiliary Variables and PushForward
Author(s): Bryant Chen*
Companies: Brex Inc.

There is growing recognition in the machine learning community that understanding causal relationships is crucial for solving a variety of pertinent problems, including policy design, sensitivity analysis, transfer learning, and fairness. Latent variables, however, can complicate causal inference by inducing problematic, spurious associations. In this talk, I will describe two techniques for negating such problematic associations in linear models, called Auxiliary Variables (AVs) and PushForward, and describe how they can be used to address the aforementioned problems.

Authors who are presenting talks have a * after their name.

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