# Efficiently Checking Actual Causality with SAT Solving

## TL;DR

This paper introduces a novel SAT-based algorithmic approach for efficiently checking causality in acyclic models with binary variables, demonstrating scalability to large models with thousands of variables.

## Contribution

It presents two SAT encodings and empirical evidence that causality can be inferred efficiently in large, complex models, addressing computational challenges in causality reasoning.

## Key findings

- Causality can be checked in less than 5 seconds for models with over 4000 variables.
- Two SAT encodings are proposed and empirically evaluated.
- The approach scales well to large, complex models.

## Abstract

Recent formal approaches towards causality have made the concept ready for incorporation into the technical world. However, causality reasoning is computationally hard; and no general algorithmic approach exists that efficiently infers the causes for effects. Thus, checking causality in the context of complex, multi-agent, and distributed socio-technical systems is a significant challenge. Therefore, we conceptualize an intelligent and novel algorithmic approach towards checking causality in acyclic causal models with binary variables, utilizing the optimization power in the solvers of the Boolean Satisfiability Problem (SAT). We present two SAT encodings, and an empirical evaluation of their efficiency and scalability. We show that causality is computed efficiently in less than 5 seconds for models that consist of more than 4000 variables.