Adversarially Balanced Representation for Continuous Treatment Effect Estimation
This addresses a practical challenge in causal inference for continuous treatments, offering an incremental improvement over existing binary-focused methods.
The paper tackles the problem of estimating individual treatment effects with continuous treatments, such as medication dosage, by proposing an adversarial counterfactual regression network that minimizes representation imbalance and maintains treatment impact, achieving empirical superiority over state-of-the-art methods on semi-synthetic datasets.
Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates. However the existing methods mostly consider the scenario of binary treatments. In this paper, we consider the more practical and challenging scenario in which the treatment is a continuous variable (e.g. dosage of a medication), and we address the two main challenges of this setup. We propose the adversarial counterfactual regression network (ACFR) that adversarially minimizes the representation imbalance in terms of KL divergence, and also maintains the impact of the treatment value on the outcome prediction by leveraging an attention mechanism. Theoretically we demonstrate that ACFR objective function is grounded in an upper bound on counterfactual outcome prediction error. Our experimental evaluation on semi-synthetic datasets demonstrates the empirical superiority of ACFR over a range of state-of-the-art methods.