9 papers
A novel decomposition to explain heterogeneity in observational and randomized studies of causality
Brian Gilbert, Ivan Dıaz, Kara E. Rudolph +2
This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized se…
Modified treatment policies that depend on the natural history of treatment
Iván DÃaz, Nicholas T. Williams, PaweÅ MorzywoÅek +1
Longitudinal modified treatment policies (LMTP) are a class of interventions that allow the definition, identification, and estimation of causal effects in general settings, such a…
Everything all at once: On choosing an estimand for multi-component environmental exposures
Kara E. Rudolph, Shodai Inose, Nicholas Williams +4
Many research questions -- particularly those in environmental health -- do not involve binary exposures. In environmental epidemiology, this includes multivariate exposure mixture…
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Nicholas Williams, Alejandro Schuler
Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stabili…
crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
Richard Liu, Nicholas T. Williams, Kara E. Rudolph +1
Causal mediation analysis is widely used to investigate how causal effects operate through specific pathways linking treatments or exposures to outcomes. Recently, \texttt{crumble}…
Riesz representers for the rest of us
Nicholas T. Williams, Oliver J. Hines, Kara E. Rudolph
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This liter…