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 …

Prediction models can perform poorly when deployed to target distributions different from the
training distribution. To understand these operational failure modes, we develop a method …

Estimating treatment effects is one of the most challenging and important tasks of data
analysts. Personalized medicine, digital marketing, and many other applications demand an …

Predicting how different interventions will causally affect a specific individual is important in
a variety of domains such as personalized medicine, public policy, and online marketing …

Using generalized random forests and rich Swedish administrative data, we show that the
earnings effects of job displacement due to establishment closures are extremely …

We propose causal isotonic calibration, a novel nonparametric method for calibrating
predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a …

Background Machine learning–based approaches that seek to accomplish individualized
treatment effect prediction have gained traction; however, some salient challenges lack …

Researchers are increasingly turning to machine learning (ML) algorithms to investigate
causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may …

Objective: To apply the latest guidance for estimating and evaluating heterogeneous
treatment effects (HTEs) in an end-to-end case study of the Long-term Anticoagulation …

Most previous studies evaluating agricultural technology adoption focus on estimating
homogeneous average treatment effects across technology adopters. Understanding the …