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On the Complexity of Identification in Linear Structural Causal Models
Julian Dörfler, Benito van der Zander, Markus Bläser +1
Learning the unknown causal parameters of a linear structural causal model is a fundamental task in causal analysis. The task, known as the problem of identification, asks to estim…
From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy
Julian Dörfler, Benito van der Zander, Markus Bläser +1
The framework of Pearl's Causal Hierarchy (PCH) formalizes three types of reasoning: probabilistic (i.e. purely observational), interventional, and counterfactual, that reflect the…
The Hardness of Reasoning about Probabilities and Causality
Benito van der Zander, Markus Bläser, Maciej Liśkiewicz
We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity p…
Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs
Marcel Wienöbst, Benito van der Zander, Maciej Liśkiewicz
Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a syst…