Representation Balancing with Decomposed Patterns for Treatment Effect Estimation
Abstract: Estimating treatment effects from observational data is subject to a covariate shift problem incurred by selection bias. Recent research has sought to mitigate this problem by balancing the distribution of representations between the treated and controlled groups. The rationale behind this is that counterfactual estimation relies on (1) preserving the predictive power of factual outcomes and (2) learning balanced representations. However, there is a trade-off between achieving these two objectives. In this paper, we propose a novel model, DIGNet, which is designed to capture the patterns that contribute to outcome prediction (task 1) and representation balancing (task 2) respectively. Specifically, we derive a theoretical upper bound that links the concept of propensity confusion to representation balancing, and further transform the balancing Patterns into Decompositions of Individual propensity confusion and Group distance minimization (PDIG) to capture more effective balancing patterns. Moreover, we suggest decomposing proxy features into Patterns of Pre-balancing and Balancing Representations (PPBR) to preserve patterns that are beneficial for outcome modeling. Extensive experiments confirm that PDIG and PPBR follow different pathways to achieve the same goal of improving treatment effect estimation. We hope our findings can be heuristics for investigating factors influencing the generalization of representation balancing models in counterfactual estimation.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Tom_Rainforth1
Submission Number: 949
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