Deep Disentangled Representation Network for Treatment Effect Estimation
This work addresses a fundamental problem in causal inference for fields like education, healthcare, and public policy, offering an incremental improvement over existing disentangled representation methods.
The paper tackles the problem of estimating individual-level treatment effects from observational data by proposing a novel algorithm that uses a mixture of experts with multi-head attention and a linear orthogonal regularizer to softly decompose pre-treatment variables, while eliminating selection bias via importance sampling re-weighting. Experimental results on semi-synthetic and real-world datasets show that the algorithm outperforms state-of-the-art methods.
Estimating individual-level treatment effect from observational data is a fundamental problem in causal inference and has attracted increasing attention in the fields of education, healthcare, and public policy.In this work, we concentrate on the study of disentangled representation methods that have shown promising outcomes by decomposing observed covariates into instrumental, confounding, and adjustment factors. However, most of the previous work has primarily revolved around generative models or hard decomposition methods for covariates, which often struggle to guarantee the attainment of precisely disentangled factors. In order to effectively model different causal relationships, we propose a novel treatment effect estimation algorithm that incorporates a mixture of experts with multi-head attention and a linear orthogonal regularizer to softly decompose the pre-treatment variables, and simultaneously eliminates selection bias via importance sampling re-weighting techniques. We conduct extensive experiments on both public semi-synthetic and real-world production datasets. The experimental results clearly demonstrate that our algorithm outperforms the state-of-the-art methods focused on individual treatment effects.