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

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 …

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize
their fairness and, in particular, the disparate impacts that ostensibly color-blind algorithms …

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 …

Decision makers, such as doctors, judges, and managers, make consequential choices
based on predictions of unknown outcomes. Do these decision makers make systematic …

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 …

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 study the problem of learning conditional average treatment effects (CATE) from high-
dimensional, observational data with unobserved confounders. Unobserved confounders …

The fundamental problem of causal inference--that we never observe counterfactuals--
prevents us from identifying how many might be negatively affected by a proposed …

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