Proximal Causal Learning of Conditional Average Treatment Effects

Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide To identify causal effects, the aforementioned approaches variety of settings ranging from medicine to marketing, operate under the exchangeability assumption, i.e., the assertion and there are a considerable number of that conditional on observed covariates, the treatment promising conditional average treatment effect assignment is as good as random. We propose a CATE estimators currently available. These, however, estimator, which using the framework of Tchetgen Tchetgen typically rely on the assumption that the measured et al. (2020), allows one to estimate causal effects in covariates are enough to justify conditional settings where conditional exchangeability fails, but one has exchangeability. We propose the P-learner, motivated measured a set of sufficient proxy variables. Our practical by the Rand DR-learner, a tailored twostage approach is motivated by the generic Neyman-orthogonal loss function for learning heterogeneous (Chernozhukov et al., 2018a) loss function from Nie & Wager treatment effects in settings where exchangeability (2021) and Kennedy (2020) that decouples nuisance given observed covariates is an implausible assumption, estimation and CATE estimation into two stages that can be and we wish to rely on proxy variables estimated (and tuned with cross-validation) by flexible lossminimizing for causal inference.