Efficient Counterfactual Reasoning in ProbLog via Single World Intervention Programs

TL;DR

This paper introduces an efficient transformation technique called SWIPs for counterfactual reasoning in ProbLog, significantly reducing inference time while maintaining accuracy, thus enhancing the robustness and interpretability of probabilistic logic programming.

Contribution

It proposes a novel program transformation method that simplifies counterfactual reasoning in ProbLog, ensuring computational efficiency and correctness under weaker independence assumptions.

Findings

Achieves 35% reduction in inference time compared to existing methods

Transforms counterfactual reasoning into marginal inference over simpler programs

Proven correctness under weaker set independence assumptions

Abstract

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 create a transformed program that (1) does not asymptotically exceed the computational complexity of existing methods, and is strictly smaller in common cases, and (2) reduces counterfactual reasoning to marginal inference over a simpler program. We formally prove the correctness of our approach, which relies on a weaker set independence assumptions and is consistent with conditional independencies, showing the resulting marginal probabilities match the counterfactual distributions of the underlying Structural Causal Model in wide domains. Our method achieves a 35\% reduction in inference time versus existing methods in extensive experiments. This work makes complex counterfactual reasoning more computationally tractable and reliable, providing a crucial step towards developing more robust and explainable AI systems. The code is at https://github.com/EVIEHub/swip.