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 …

Social scientists seeking to inform policy or public action must carefully consider how to
identify effects and express inferences because actions based on invalid inferences may not …

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 …

Estimating heterogeneous treatment effects from observational data is a crucial task across
many fields, hel** policy and decision-makers take better actions. There has been recent …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a
broad class of causal parameters that can be identified as linear functionals of the …

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 …

We address the estimation of conditional average treatment effects (CATEs) for structured
treatments (eg, graphs, images, texts). Given a weak condition on the effect, we propose the …

Estimating heterogeneous treatment effects (HTE) from observational studies is rising in
importance due to the widespread accumulation of data in many fields. Due to the selection …

Maintenance is a challenging operational problem where the goal is to plan sufficient
preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically …

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