When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

Estimating treatment effects conditional on observed covariates can improve the ability to
tailor treatments to particular individuals. Doing so effectively requires dealing with potential …

There has been an increase in interest in experimental evaluations to estimate causal
effects, partly because their internal validity tends to be high. At the same time, as part of the …

We study the identification and estimation of long-term treatment effects when both
experimental and observational data are available. Since the long-term outcome is …

We are interested in estimating the effect of a treatment applied to individuals at multiple
sites, where data is stored locally for each site. Due to privacy constraints, individual‐level …

We consider the problem of combining data from observational and experimental sources to
draw causal conclusions. To derive combined estimators with desirable properties, we …

The ability to generalize experimental results from randomized control trials (RCTs) across
locations is crucial for informing policy decisions in targeted regions. Such generalization is …

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 …

With climate change threatening agricultural productivity and global food demand
increasing, it is important to better understand which farm management practices will …

Federated learning of causal estimands may greatly improve estimation efficiency by
leveraging data from multiple study sites, but robustness to heterogeneity and model …