Social scientists commonly use computational models to estimate proxies of unobserved
concepts, then incorporate these proxies into subsequent tests of their theories. The …

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 …

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 consider the problem of partial identification, the estimation of bounds on the treatment
effects from observational data. Although studied using discrete treatment variables or in …

When an exposure of interest is confounded by unmeasured factors, an instrumental
variable (IV) can be used to identify and estimate certain causal contrasts. Identification of …

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

Abstract Randomized Controlled Trials (RCTs) represent a gold standard when developing
policy guidelines. However, RCTs are often narrow, and lack data on broader populations of …

Estimating dynamic treatment regimes (DTRs) from retrospective observational data is
challenging as some degree of unmeasured confounding is often expected. In this work, we …

Causal inference from observational datasets often relies on measuring and adjusting for
covariates. In practice, measurements of the covariates can often be noisy and/or biased, or …

We address the problem of integrating data from multiple, possibly biased, observational
and interventional studies, to eventually compute counterfactuals in structural causal …