3 papers
stat.ME2026
A cautious approach to constraint-based causal model selection
Daniel Malinsky
We study the data-driven selection of causal graphical models using constraint-based algorithms, which determine the existence or non-existence of edges (causal connections) in a g…
stat.ME2025
Post-selection inference for causal effects after causal discovery
Ting-Hsuan Chang, Zijian Guo, Daniel Malinsky
Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of…
stat.ME2025
Mediated probabilities of causation
Max Rubinstein, Maria Cuellar, Daniel Malinsky
We propose a set of causal estimands that we call the "mediated probabilities of causation." These estimands quantify the probabilities that an observed negative outcome was induce…