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stat.ME2026
Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death
Ruixuan Zhao, Mats Stensrud, Linbo Wang
In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent events such as survival.…
stat.ME2026
Estimating the Wasserstein barycenter of one-dimensional distributions under sparse sampling
James Peng, Florian Stijven, Linbo Wang +1
We study distributional data under sparse sampling where each unit is represented by a probability distribution on the real line observed only through a small i.i.d.~sample. A natu…
stat.OT2026
A Distributional Perspective on Pearl's Causal Hierarchy: From Marginal to Joint and Individualized Potential Outcomes
Peng Wu, Linbo Wang
Pearl's causal hierarchy is a foundational lens for formulating causal questions and is most often discussed within the framework of structural causal models. We recast the hierarc…