The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

Importance Many medical journals, includingJAMA, restrict the use of causal language to the
reporting of randomized clinical trials. Although well-conducted randomized clinical trials …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Learning individual-level causal effects from observational data, such as inferring the most
effective medication for a specific patient, is a problem of growing importance for policy …

A standard assumption for causal inference from observational data is that one has
measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects …

An introduction to the emerging fusion of machine learning and causal inference. The book
presents ideas from classical structural equation models (SEMs) and their modern AI …

Abstract Purpose of Review Negative controls are a powerful tool to detect and adjust for
bias in epidemiological research. This paper introduces negative controls to a broader …

An Introduction to Proximal Causal Inference Page 1 Statistical Science 2024, Vol. 39, No. 3,
375–390 https://doi.org/10.1214/23-STS911 © Institute of Mathematical Statistics, 2024 An …

Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post
with the author's gender affect the post popularity? This paper develops a method to …