Counterfactual Representation Learning with Balancing Weights
- URL: http://arxiv.org/abs/2010.12618v2
- Date: Wed, 24 Feb 2021 03:01:10 GMT
- Title: Counterfactual Representation Learning with Balancing Weights
- Authors: Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta,
Ricardo Henao, Fan Li, Lawrence Carin
- Abstract summary: Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
Recent literature has explored representation learning to achieve this goal.
We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
- Score: 74.67296491574318
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A key to causal inference with observational data is achieving balance in
predictive features associated with each treatment type. Recent literature has
explored representation learning to achieve this goal. In this work, we discuss
the pitfalls of these strategies - such as a steep trade-off between achieving
balance and predictive power - and present a remedy via the integration of
balancing weights in causal learning. Specifically, we theoretically link
balance to the quality of propensity estimation, emphasize the importance of
identifying a proper target population, and elaborate on the complementary
roles of feature balancing and weight adjustments. Using these concepts, we
then develop an algorithm for flexible, scalable and accurate estimation of
causal effects. Finally, we show how the learned weighted representations may
serve to facilitate alternative causal learning procedures with appealing
statistical features. We conduct an extensive set of experiments on both
synthetic examples and standard benchmarks, and report encouraging results
relative to state-of-the-art baselines.
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