Lifted Causal Inference

Abstract

Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we show how lifting can be applied to efficiently compute causal effects in relational domains. More specifically, we introduce parametric causal factor graphs (PCFGs) to incorporate causal knowledge in lifted models and give a formal semantics of interventions therein. We further present the Lifted Causal Inference (LCI) algorithm to compute causal effects on a lifted level, thereby drastically speeding up causal inference compared to propositional inference, e.g., in causal Bayesian networks. In addition, we present partially directed parametric causal factor graphs (PD-PCFGs) as a generalisation of PCFGs to handle partial causal knowledge and extend LCI to perform lifted causal inference in a PD-PCFG, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships.

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BibTeX Citation

@article{Luttermann2026a,
    author    = {Malte Luttermann and Tanya Braun and Ralf Möller and Marcel Gehrke},
    title     = {{Lifted Causal Inference}},
    journal   = {Annals of Mathematics and Artificial Intelligence},
    volume    = {},
    year      = {2026},
    pages     = {},
    publisher = {Springer},
}