activity
20202026
most citedRisk-based decision making: estimands for sequential prediction under interventions

4 citations · 9 across the 9 of their papers we have counts for

collaborators

9 papers

stat.ME2026

Partial Identification Learning with Categorical Treatments for Individualized Treatment Rules

Johannes Hruza, Paweł Morzywołek, Jakob Zeitler +2

We develop a partial identification learning framework for individualized treatment rules (ITRs) with categorical treatments, outcomes, and instrumental variables. Rather than rely…

stat.ME2026

Modified treatment policies that depend on the sequence of natural treatment values

Iván Díaz, Nicholas T. Williams, Paweł Morzywołek +1

Longitudinal modified treatment policies (LMTPs) are a class of interventions that allow the definition, identification, and estimation of causal effects in general settings, inclu…

stat.ME2025

Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond

Pawel Morzywolek, Peter B. Gilbert, Alex Luedtke

We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, w…

stat.ME2024★ 1 cited

The risks of risk assessment: causal blind spots when using prediction models for treatment decisions

Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…

stat.ME2023★ 4 cited

Risk-based decision making: estimands for sequential prediction under interventions

Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…

stat.ME2023★ 1 cited

Orthogonal prediction of counterfactual outcomes

Stijn Vansteelandt, Paweł Morzywołek

Orthogonal meta-learners, such as DR-learner, R-learner and IF-learner, are increasingly used to estimate conditional average treatment effects. They improve convergence rates rela…

stat.ME2023★ 3 cited

On Weighted Orthogonal Learners for Heterogeneous Treatment Effects

Pawel Morzywolek, Johan Decruyenaere, Stijn Vansteelandt

Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatment effect heterogeneity in term…

stat.AP2021

On Estimation and Cross-validation of Dynamic Treatment Regimes with Competing Risks

Pawel Morzywolek, Johan Steen, Wim Van Biesen +2

The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomised co…

stat.ME2020

Handling time-dependent exposures and confounders when estimating attributable fractions -- bridging the gap between multistate and counterfactual modeling

Johan Steen, Pawel Morzywolek, Wim Van Biesen +2

The population-attributable fraction (PAF) expresses the proportion of events that can be ascribed to a certain exposure in a certain population. It can be strongly time-dependent…