Counterfactual Regression with Importance Sampling Weights
Full Text:
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Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under various treatment options. For a binary-treatment case, this translates into estimating the difference between the outcomes (e.g., survival time) of the two available treatment options – i.e., her Individual Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data has selection bias: the treatment assigned to a patient depends on that patient's attributes. In this work, we borrow ideas from domain adaptation to address the distributional shift between the source (outcome of the administered treatment, appearing in the observed training data) and target (outcome of the alternative treatment) that exists due to selection bias. We propose a context-aware importance sampling re-weighing scheme, built on top of a representation learning module, for estimating ITEs. Empirical results on two publicly available benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art.
Citation
N. Hassanpour,
R. Greiner.
"Counterfactual Regression with Importance Sampling Weights". International Joint Conference on Artificial Intelligence (IJCAI), pp 5880-5887, August 2019.
| Keywords: |
counterfactual reasoning, machine learning |
| Category: |
In Conference |
| Web Links: |
IJCAI |
BibTeX
@incollection{Hassanpour+Greiner:IJCAI19,
author = {Negar Hassanpour and Russ Greiner},
title = {Counterfactual Regression with Importance Sampling Weights},
Pages = {5880-5887},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2019,
}Last Updated: June 28, 2020
Submitted by Russ Greiner 
