The paper introduces causal foundation models that can bound the effects of interventions and counterfactuals using only observational data. It defines a canonical prior with full support over structural causal models with discrete observables, enabling the translation of counterfactual bounding into learning distributions over functions that map data and structural assumptions to causal queries. This approach extends causal foundational modelling to partially-identifiable causal effects, where unobserved confounding leads to multiple compatible values for the effect.
By Alexis Bellot, Anish Dhir
arXiv:2505. 15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making.
By Xin Shu, Shuai Wang, Ang Li
arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.
By Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
The paper tackles selecting a cost‑constrained set of experiments that most effectively tighten bounds on a partially identifiable causal query. It formalizes this as the NP‑hard max‑potency problem, introduces efficient graphical pruning rules to reduce the search space, and demonstrates the approach on synthetic graphs and real NHANES data to estimate the effect of physical activity on diabetes.
By Tobias Maringgele, Jalal Etesami
The paper studies observational dominance among causal structures with latent variables, defining one structure as dominating another if it can realize all distributions that the other can over the same visible variables. It provides a full characterization of this dominance partial order for three visible variables and a partial one for four, and shows that many equivalence classes are distinguished by nontrivial inequality constraints similar to Bell or instrumental inequalities. The authors also demonstrate that constraint‑based causal discovery algorithms relying only on conditional independence are much less powerful than those incorporating nested Markov and inequality constraints.
By Marina Maciel Ansanelli, Elie Wolfe, Robert W. Spekkens
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf