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

Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …

We investigate the problem of machine learning-based (ML) predictive inference on
individual treatment effects (ITEs). Previous work has focused primarily on developing ML …

Decision making or scientific discovery pipelines such as job hiring and drug discovery often
involve multiple stages: before any resource-intensive step, there is often an initial screening …

Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

Matrix completion aims to estimate missing entries in a data matrix, using the assumption of
a low-complexity structure (eg, low-rankness) so that imputation is possible. While many …

Estimating an individual treatment effect (ITE) is essential to personalized decision making.
However, existing methods for estimating the ITE often rely on unconfoundedness, an …

We consider the problem of constructing bounds on the average treatment effect (ATE) when
unmeasured confounders exist but have bounded influence. Specifically, we assume that …

This paper studies offline policy learning, which aims at utilizing observations collected a
priori (from either fixed or adaptively evolving behavior policies) to learn the optimal …