Sufficient Dimension Reduction for Average Causal Effect Estimation
- URL: http://arxiv.org/abs/2009.06444v1
- Date: Mon, 14 Sep 2020 13:58:57 GMT
- Title: Sufficient Dimension Reduction for Average Causal Effect Estimation
- Authors: Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu
- Abstract summary: Having a large number of covariates can have a negative impact on the quality of causal effect estimation.
We develop an algorithm which employs a supervised kernel dimension reduction method to search for a lower dimensional representation.
The proposed algorithm is evaluated on two semi-synthetic and three real-world datasets.
- Score: 21.029760577643554
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Having a large number of covariates can have a negative impact on the quality
of causal effect estimation since confounding adjustment becomes unreliable
when the number of covariates is large relative to the samples available.
Propensity score is a common way to deal with a large covariate set, but the
accuracy of propensity score estimation (normally done by logistic regression)
is also challenged by large number of covariates. In this paper, we prove that
a large covariate set can be reduced to a lower dimensional representation
which captures the complete information for adjustment in causal effect
estimation. The theoretical result enables effective data-driven algorithms for
causal effect estimation. We develop an algorithm which employs a supervised
kernel dimension reduction method to search for a lower dimensional
representation for the original covariates, and then utilizes nearest neighbor
matching in the reduced covariate space to impute the counterfactual outcomes
to avoid large-sized covariate set problem. The proposed algorithm is evaluated
on two semi-synthetic and three real-world datasets and the results have
demonstrated the effectiveness of the algorithm.
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