Learning Disentangled Representations for CounterFactual Regression
- Negar Hassanpour, Department of Computing Science, University of Alberta
- Russ Greiner, Dept of Computing Science; PI of AICML
We consider the challenge of estimating causal effects from observational data; and note that, in general, only some factors based on the observed covariates X contribute to selection of the treatment T, and only some to determining the outcomes Y. We model this by considering three underlying sources of {X T, Y} and show that explicitly modeling these sources offers great insight to guide designing models that better handle selection bias. This paper is an attempt to conceptualize this line of thought and provide a path to explore it further. In this work, we propose an algorithm to (1) identify disentangled representations of the above-mentioned underlying factors from any observational dataset D and (2) leverage this knowledge to reduce, as well as account for, the negative impact of selection bias on estimating the causal effects from D. Our empirical results show that the proposed method (i) achieves state-of-the-art performance in both individual and population based evaluation measures and (ii) is highly robust under various data generating scenarios.
Citation
N. Hassanpour, R. Greiner. "Learning Disentangled Representations for CounterFactual Regression". NeurIPS Workshop on Causal ML, December 2019.| Keywords: | Counterfactual Regression, Causal Effect Estimation, Selection Bias, Off-policy Learning |
| Category: | In Workshop |
BibTeX
@misc{Hassanpour+Greiner:CausalML19,
author = {Negar Hassanpour and Russ Greiner},
title = {Learning Disentangled Representations for CounterFactual Regression},
booktitle = {NeurIPS Workshop on Causal ML},
year = 2019,
}Last Updated: July 14, 2020Submitted by Sabina P
