We investigate the problem of machine learning-based (ML) predictive inference on
individual treatment effects (ITEs). Previous work has focused primarily on developing ML …

Shortcut learning, in which models make use of easy-to-represent but unstable associations,
is a major failure mode for robust machine learning. We study a flexible, causally-motivated …

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 …

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 …

Abstract Domain shift, encountered when using a trained model for a new patient
population, creates significant challenges for sequential decision making in healthcare since …

Estimating the effect of a treatment on an individual's outcome of interest is an important
challenge in various fields, such as healthcare, economics, marketing, and education …

Finding an effective medical treatment often requires a search by trial and error. Making this
search more efficient by minimizing the number of unnecessary trials could lower both costs …

Estimating treatment effects from observational data is often subject to a covariate shift
problem incurred by selection bias. Recent research has sought to mitigate this problem by …

Observational studies often seek to infer the causal effect of a treatment even though both
the assigned treatment and the outcome depend on other confounding variables. An …

Differential measurement error, which occurs when the error in the measured outcome is
correlated with the treatment renders the causal effect unidentifiable from observational …