Estimating heterogeneous treatment effects from observational data is a crucial task across
many fields, helping 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 …

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

In the era of fast-paced precision medicine, observational studies play a major role in
properly evaluating new treatments in clinical practice. Yet, unobserved confounding can …

The empirical risk minimization approach to data-driven decision making assumes that we
can learn a decision rule from training data drawn under the same conditions as the ones …

Unobserved confounding is common in many applications, making causal inference from
observational data challenging. As a remedy, causal sensitivity analysis is an important tool …

Weighting methods are popular tools for estimating causal effects, and assessing their
robustness under unobserved confounding is important in practice. Current approaches to …

Because the average treatment effect (ATE) measures the change in social welfare, even if
positive, there is a risk of negative effect on, say, some 10% of the population. Assessing …

Offline reinforcement learning is important in domains such as medicine, economics, and e-
commerce where online experimentation is costly, dangerous or unethical, and where the …

We consider the problem of estimating a dose-response curve, both globally and locally at a
point. Continuous treatments arise often in practice, eg in the form of time spent on an …