Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

We consider the problem of combining data from observational and experimental sources to
draw causal conclusions. To derive combined estimators with desirable properties, we …

One of the great promises of applying machine learning to clinical data is the possibility of
learning optimal per-patient treatment rules. The goal is to use data collected in clinical …

Causal inference is the process of using assumptions, study designs, and estimation
strategies to draw conclusions about the causal relationships between variables based on …

Subgroup analysis of treatment effects plays an important role in applications from medicine
to public policy to recommender systems. It allows physicians (for example) to identify …

Black-box heterogeneous treatment effect (HTE) models are increasingly being used to
create personalized policies that assign individuals to their optimal treatments. However …

There has been recent interest by payers, health care systems, and researchers in the
development of machine learning and artificial intelligence models that predict an …

The concept of causality plays a significant role in human cognition. In the past few decades,
causal effect estimation has been well developed in many fields, such as computer science …

Learning heterogeneous treatment effects (HTEs) is an important problem across many
fields. Most existing methods consider the setting with a single treatment arm and a single …

Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for
individuals when releasing aggregated statistics or building statistical/machine learning …