On learning disentangled representations for individual treatment effect estimation

Objective: Estimating the individualized treatment effect (ITE) from observational data is a challenging task due to selection bias, which results from the distributional discrepancy between different treatment groups caused by the dependence between features and assigned treatments. This dependence is induced by the factors related to the treatment assignment. We hypothesize that features consist of three types of latent factors: outcome-specific factors, treatment-specific factors and confounders. Then, we aim to reduce the influence of treatment-related factors, i.e., treatment-specific factors and confounders, on outcome prediction to mitigate the effects of selection bias. Method: We present a novel representation learning model in which both the main task of outcome prediction and the auxiliary task of classifying the treatment assignment are used to learn the outcome-oriented and treatment-oriented latent representations, respectively. However, since the confounders are related to both treatment assignment and outcome, it is still contained in the representations. To further reduce influence of the confounders contained in both representations, individualized orthogonal regularization is incorporated into the proposed model. The orthogonal regularization forces the outcome-oriented and treatment-oriented latent representations of an individual to be vertical in the inner product space, meaning they are orthogonal with each other, and the common information of confounder is reduced. Such that the ITE can be estimated more precisely without the effects of selection bias. Result: We evaluate our proposed model on a semi-simulated dataset and a real-world dataset. The experimental results demonstrate that the proposed model achieves competitive or better performance compared with the performances of the state-of-the-art models. Conclusion: The proposed method is well performed on ITE estimation with the ability to reduce selection bias thoroughly by incorporating an auxiliary task and adopting orthogonal regularization to disentangle the latent factors. Significance: This paper offers a novel method of reducing selection bias in estimating the ITE from observational data by disentangled representation learning.