# Argumentative Causal Discovery

arXiv: https://arxiv.org/abs/2405.11250 · HTML: https://mlsanity.com/paper/2405.11250/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 11 of 11)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Gradient-Based Neural DAG Learning](https://mlsanity.com/paper/1906.02226/) (2019)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Learning Sparse Nonparametric DAGs](https://mlsanity.com/paper/1909.13189/) (2019)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Learning Equivalence Classes of Bayesian Networks Structures](https://mlsanity.com/paper/1302.3566/) (2013)
- [Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation](https://mlsanity.com/paper/2404.11431/) (2024)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [Non-flat ABA is an Instance of Bipolar Argumentation](https://mlsanity.com/paper/2305.12453/) (2023)
- [Learning Chordal Markov Networks by Constraint Satisfaction](https://mlsanity.com/paper/1310.0927/) (2013)
- [On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head](https://mlsanity.com/paper/2405.09415/) (2024)

## Built on by (most cited first, 3 of 3)

- [On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models](https://mlsanity.com/paper/2502.08531/) (2025)
- [Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach](https://mlsanity.com/paper/2602.16481/) (2026)
- [Constrained Assumption-Based Argumentation Frameworks](https://mlsanity.com/paper/2602.13135/) (2026)

## Abstract (arXiv)

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.