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 …

In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

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 …

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 …

Statistical risk assessments inform consequential decisions such as pretrial release in
criminal justice, and loan approvals in consumer finance. Such risk assessments make …

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 …

Sensitivity analysis for the unconfoundedness assumption is a crucial component of
observational studies. The marginal sensitivity model has become increasingly popular for …

In classical causal inference, inferring cause-effect relations from data relies on the
assumption that units are independent and identically distributed. This assumption is …

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 …

Consider sensitivity analysis for estimating average treatment effects under unmeasured
confounding, assumed to satisfy a marginal sensitivity model. At the population level, we …