Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

This article reviews recent advances in causal inference relevant to sociology. We focus on
a selective subset of contributions aligning with four broad topics: causal effect identification …

Orthogonal statistical learning Page 1 The Annals of Statistics 2023, Vol. 51, No. 3, 879–908
https://doi.org/10.1214/23-AOS2258 © Institute of Mathematical Statistics, 2023 ORTHOGONAL …

There are a number of available methods for selecting whom to prioritize for treatment,
including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We …

We propose strategies to estimate and make inference on key features of heterogeneous
effects in randomized experiments. These key features include best linear predictors of the …

This paper provides estimation and inference methods for the best linear predictor
(approximation) of a structural function, such as conditional average structural and treatment …

Given the unconfoundedness assumption, we propose new nonparametric estimators for the
reduced dimensional conditional average treatment effect (CATE) function. In the first stage …

Trustworthy policy learning has significant importance in making reliable and harmless
treatment decisions for individuals. Previous policy learning approaches aim at the well …

We propose a nonparametric inference method for causal effects of continuous treatment
variables, under unconfoundedness and nonparametric or high-dimensional nuisance …

This paper reviews, applies, and extends recently proposed methods based on double
machine learning (DML) with a focus on programme evaluation under unconfoundedness …