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Topic:Causal Inference by Encoding Generative Modeling
Date:24/11/2023
Time:2:30 pm - 3:30 pm
Venue:LT2, Lady Shaw Building, The Chinese University of Hong Kong
Category:Distinguished Lecture
Speaker:Professor Wing Hung WONG
PDF:20231124-DL-WHWong-A3.pdf
Details:

Abstract

We consider the problem of inferring the causal effect of an exposure variable X on an outcome variable Y. Besides X and Y, a high-dimensional covariate V is also measured. It is assumed that confounding variables that may cause bias in the desired causal inference are low-dimensional features of V. Under this assumption, we propose an encoding generative modeling (EGM) approach for the estimation of the average dose response function, a function that captures, in an average sense, the dependency of Y on X when confounders were held fixed. We show that EGM provides a framework for us to develop deep learning-based estimates for the structural equations that describe the causal relations among the variables. We will present numerical and theoretical evidence to demonstrate the effectiveness of our approach.

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