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pro vyhledávání: '"Stensrud"'
Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. Th
Externí odkaz:
http://arxiv.org/abs/2410.00985
Autor:
Perenyi, Gellert, Stensrud, Mats J.
Pathogens usually exist in heterogeneous variants, like subtypes and strains. Quantifying treatment effects on the different variants is important for guiding prevention policies and treatment development. Here we ground analyses of variant-specific
Externí odkaz:
http://arxiv.org/abs/2408.07560
The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials mi
Externí odkaz:
http://arxiv.org/abs/2406.10360
Autor:
Janvin, Matias, Stensrud, Mats J.
Knowing whether vaccine protection wanes over time is important for health policy and drug development. However, quantifying waning effects is difficult. A simple contrast of vaccine efficacy at two different times compares different populations of i
Externí odkaz:
http://arxiv.org/abs/2405.01336
Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, informative bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cov
Externí odkaz:
http://arxiv.org/abs/2404.11510
Policy-makers are often faced with the task of distributing a limited supply of resources. To support decision-making in these settings, statisticians are confronted with two challenges: estimands are defined by allocation strategies that are functio
Externí odkaz:
http://arxiv.org/abs/2403.19842
Autor:
Sarvet, Aaron L., Stensrud, Mats J.
In our original article (Sarvet & Stensrud, 2024), we examine twin definitions of "harm" in personalized medicine: one based on predictions of individuals' unmeasurable response types (counterfactual harm), and another based solely on the observation
Externí odkaz:
http://arxiv.org/abs/2403.14869
Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, defining, identifying, and estimating causal effects conditional on post-treatment events requires care, even in perfectly execute
Externí odkaz:
http://arxiv.org/abs/2402.11020
We formalize an interpretational error that is common in statistical causal inference, termed identity slippage. This formalism is used to describe historically-recognized fallacies, and analyse a fast-growing literature in statistics and applied fie
Externí odkaz:
http://arxiv.org/abs/2312.07610