From Checking to Inference: Actual Causality Computations as Optimization Problems

TL;DR

This paper introduces a novel optimization-based framework for automating actual causality reasoning using ILP and MaxSAT encodings, enabling efficient analysis of large models for causality detection and inference.

Contribution

It formulates causality reasoning as optimization problems with new ILP and MaxSAT encodings, improving automation and scalability over previous methods.

Findings

MaxSAT outperforms ILP in causality checking tasks.

Causality inference can be performed efficiently without candidate causes.

Models with over 8000 variables are processed in seconds to minutes.

Abstract

Actual causality is increasingly well understood. Recent formal approaches, proposed by Halpern and Pearl, have made this concept mature enough to be amenable to automated reasoning. Actual causality is especially vital for building accountable, explainable systems. Among other reasons, causality reasoning is computationally hard due to the requirements of counterfactuality and the minimality of causes. Previous approaches presented either inefficient or restricted, and domain-specific, solutions to the problem of automating causality reasoning. In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations. We contribute and compare two compact, non-trivial, and sound integer linear programming (ILP) and Maximum Satisfiability (MaxSAT) encodings to check causality. Given a candidate cause, both approaches identify what a minimal cause is. Also, we present an ILP encoding to infer causality without requiring a candidate cause. We show that both notions are efficiently automated. Using models with more than $8000$ variables, checking is computed in a matter of seconds, with MaxSAT outperforming ILP in many cases. In contrast, inference is computed in a matter of minutes.