Causal Effect Estimation using Variational Information Bottleneck
- URL: http://arxiv.org/abs/2110.13705v1
- Date: Tue, 26 Oct 2021 13:46:12 GMT
- Title: Causal Effect Estimation using Variational Information Bottleneck
- Authors: Zhenyu Lu, Yurong Cheng, Mingjun Zhong, George Stoian, Ye Yuan and
Guoren Wang
- Abstract summary: Causal inference is to estimate the causal effect in a causal relationship when intervention is applied.
We propose a method to estimate Causal Effect by using Variational Information Bottleneck (CEVIB)
- Score: 19.6760527269791
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Causal inference is to estimate the causal effect in a causal relationship
when intervention is applied. Precisely, in a causal model with binary
interventions, i.e., control and treatment, the causal effect is simply the
difference between the factual and counterfactual. The difficulty is that the
counterfactual may never been obtained which has to be estimated and so the
causal effect could only be an estimate. The key challenge for estimating the
counterfactual is to identify confounders which effect both outcomes and
treatments. A typical approach is to formulate causal inference as a supervised
learning problem and so counterfactual could be predicted. Including linear
regression and deep learning models, recent machine learning methods have been
adapted to causal inference. In this paper, we propose a method to estimate
Causal Effect by using Variational Information Bottleneck (CEVIB). The
promising point is that VIB is able to naturally distill confounding variables
from the data, which enables estimating causal effect by using observational
data. We have compared CEVIB to other methods by applying them to three data
sets showing that our approach achieved the best performance. We also
experimentally showed the robustness of our method.
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