Treatment effect estimation with disentangled latent factors

Anonymous Abstract A pressing concern faced by cancer patients is their prognosis under different treatment options. Considering a binary-treatment, e.g., to receive radiotherapy or not, the problem can be characterized as estimating the treatment effect of radiotherapy on the survival outcome of the patients. Estimating treatment effect from observational studies is a fundamental problem, yet it is still especially challenging due to the counterfactual and confounding problems. In this work, we show the importance of differentiating confounding factors from factors that only affect the treatment or the outcome, and propose a data-driven approach to learn and disentangle the latent factors into three disjoint sets for a more accurate estimating treatment estimator. Empirical validations on semisynthetic benchmark and real-world datasets demonstrate the effectiveness of the proposed method. 1 Introduction A fundamental question in many scientific researches can be stated as: whether and how much an intervention affect the result of an outcome? In other words, in the case of a binary treatment, whether and to what degree the outcome without the treatment differs from the outcome with the treatment? In social economy, policy makers need to study whether a job training program will improve employment perspective of the workers [ Athey and Imbens, 2016 ]; in cancer diagnosis, oncologists need to determine whether prescribing a treatment will improve patients' prognoses [ Zhang et al., 2017 ] . In the center of these questions lies the counterfactual problem: each individual is associated with two potential outcomes: one with treatment and one without.