In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …

One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …

In this essay I discuss potential outcome and graphical approaches to causality, and their
relevance for empirical work in economics. I review some of the work on directed acyclic …

We consider the task of counterfactual estimation from observational imaging data given a
known causal structure. In particular, quantifying the causal effect of interventions for high …

This essay describes the evolution and recent convergence of two methodological
approaches to causal inference. The first one, in statistics, started with the analysis and …

We investigate how to exploit structural similarities of an individual's potential outcomes
(POs) under different treatments to obtain better estimates of conditional average treatment …

Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …