3 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference
Johan de Aguas, Sebastian Krumscheid, Johan Pensar +1
We define the Probability of Tiered Benefit in scenarios with a binary exposure and an outcome that is either categorical with ordered tiers or continuous partitioned by…
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…