1 citations · 2 across the 8 of their papers we have counts for
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Modified treatment policies that depend on the natural history of treatment
Iván Díaz, Nicholas T. Williams, Paweł Morzywołek +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…
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}…
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…
Longitudinal weighted and trimmed treatment effects with flip interventions
Alec McClean, Alexander W. Levis, Nicholas Williams +1
Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily g…
Longitudinal Generalizations of the Average Treatment Effect on the Treated for Multi-valued and Continuous Treatments
Herbert Susmann, Nicholas T. Williams, Kara E. Rudolph +1
The Average Treatment Effect on the Treated (ATT) is a common causal parameter defined as the average effect of a binary treatment among the subset of the population receiving trea…