Lifted Causal Inference

2026-06-26

AI Summary

This work addresses the high computational complexity of causal effect estimation in relational domains by introducing lifted inference to causal reasoning for the first time. It proposes the Parametrized Causal Factor Graph (PCFG) and its partially directed extension (PD-PCFG), along with the Lifted Causal Inference (LCI) algorithm. By structurally modeling intervention semantics, the approach enables efficient and exact inference even under incomplete causal knowledge. Compared to traditional propositional-level causal Bayesian networks, LCI achieves substantial gains in computational efficiency within relational settings while preserving inference accuracy.

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.

Problem

• lifted inference
• causal inference
• relational domains
• causal effects
• partial causal knowledge

Innovation

• lifted inference
• causal inference
• parametric causal factor graphs
• relational domains
• intervention semantics