3 papers
stat.ME2025
Confidence Regions for Multiple Outcomes, Effect Modifiers, and Other Multiple Comparisons
Paul N Zivich, Stephen R Cole, Noah Greifer +3
In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation p…
stat.ME2025
Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
Rachael K. Ross, Lina M. Montoya, Dana E. Goin +2
Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can miti…
stat.ME2025
Pulling back the curtain: the road from statistical estimand to machine-learning based estimator for epidemiologists (no wizard required)
Audrey Renson, Lina Montoya, Dana E. Goin +2
Epidemiologists increasingly use causal inference methods that rely on machine learning, as these approaches can relax unnecessary model specification assumptions. While deriving a…