3 citations · 3 across the 5 of their papers we have counts for
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stat.ME2025
Causal inference amid missingness-specific independencies and mechanism shifts
Johan de Aguas, Leonard Henckel, Johan Pensar +1
The recovery of causal effects in structural models with missing data often relies on -graphs, which assume that missingness mechanisms do not directly influence substantive var…
stat.ME2025
Bayesian estimation of causal effects from observational categorical data
Vera Kvisgaard, Johan Pensar
We present a Bayesian procedure for estimation of pairwise intervention effects in a high-dimensional system of categorical variables. We assume that we have observational data gen…
stat.ME2024★ 3 cited
Recovery and inference of causal effects with sequential adjustment for confounding and attrition
Johan de Aguas, Johan Pensar, Tomás Varnet Pérez +1
Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missin…