Argumentative Causal Discovery

TL;DR

This paper introduces a novel causal discovery method using assumption-based argumentation and symbolic reasoning, capable of accurately identifying causal graphs from data, and demonstrates competitive performance on benchmark datasets.

Contribution

It presents a new approach combining ABA with causality theories for causal discovery, with proven properties and empirical validation.

Findings

Method retrieves ground-truth causal graphs under natural conditions.

Performs well compared to established baselines on benchmark datasets.

Uses answer set programming for implementation.

Abstract

Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data. We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs. We also conduct experiments with an implementation of our method in answer set programming (ASP) on four datasets from standard benchmarks in causal discovery, showing that our method compares well against established baselines.