Efficient Counterfactual Reasoning in ProbLog via Single World Intervention Programs
Probabilistic Logic Programming (PLP) languages, like ProbLog, naturally support reasoning under uncertainty, while maintaining a declarative and interpretable framework. Meanwhile, counterfactual reasoning (i.e., answering ``what if'' questions) is critical for ensuring AI systems are robust and trustworthy; however, integrating this capability into PLP can be computationally prohibitive and unstable in accuracy. This paper addresses this challenge, by proposing an efficient program transformation for counterfactuals as Single World Intervention Programs (SWIPs) in ProbLog. By systematically splitting ProbLog clauses to observed and fixed components relevant to a counterfactual, we creat...
| Authors | Saimun Habib, Vaishak Belle, Fengxiang He |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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