Estimating Average Treatment Effects via Orthogonal Regularization
This work addresses the challenge of causal inference for decision-makers in fields like healthcare or policy, offering an incremental improvement over existing methods by incorporating unconfoundedness constraints more effectively.
The paper tackles the problem of estimating average treatment effects from observational data by proposing a regularization framework that enforces orthogonality constraints based on unconfoundedness, resulting in a method called DONUT that substantially outperforms state-of-the-art approaches on benchmark datasets.
Decision-making often requires accurate estimation of treatment effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have to be estimated. Previous methods estimate outcomes based on unconfoundedness but neglect any constraints that unconfoundedness imposes on the outcomes. In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness. To this end, we formalize unconfoundedness as an orthogonality constraint, which ensures that the outcomes are orthogonal to the treatment assignment. This orthogonality constraint is then included in the loss function via a regularization. Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT), which learn outcomes that are orthogonal to the treatment assignment. Using a variety of benchmark datasets for estimating average treatment effects, we demonstrate that DONUT outperforms the state-of-the-art substantially.