Primary Submission Category: Machine Learning and Causal Inference
C-Learner: Constrained Learning for Causal Inference
Authors: Tianhui (Tiffany) Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong,
Presenting Author: Tianhui (Tiffany) Cai*
A fundamental problem in causal inference is the accurate estimation of the average treatment effect (ATE). Existing methods such as Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE) are asymptotically optimal. Although these methods are asymptotically equivalent, they exhibit significant differences in finite-sample performance, numerical stability, and complexity, which raises questions about their relative practical utility.
In response, we develop the Constrained Learner (C-Learner), which is a new asymptotically optimal method for estimating the ATE. C-Learner is flexible and conceptually very simple: it directly encodes the condition for asymptotic optimality of the estimator as a constraint for learning outcome models, which are then used in a plug-in estimator for the ATE. C-Learner can thus leverage tools and advances from constrained optimization to learn these outcome models. In practice, we find that C-Learner performs comparably to or better than other asymptotically optimal methods. These attributes collectively position C-Learner as a compelling new tool for researchers and practitioners of causal inference.