# A Sound and Complete Algorithm for Learning Causal Models from Relational Data

arXiv: https://arxiv.org/abs/1309.6843 · HTML: https://mlsanity.com/paper/1309.6843/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2013.

## Built on by (most cited first, 12 of 12)

- [Lifted Causal Inference in Relational Domains](https://mlsanity.com/paper/2403.10184/) (2024)
- [Learning Relational Causal Models with Cycles through Relational Acyclification](https://mlsanity.com/paper/2208.12210/) (2022)
- [Directed Acyclic Graph Structure Learning from Dynamic Graphs](https://mlsanity.com/paper/2211.17029/) (2022)
- [Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence](https://mlsanity.com/paper/2305.17479/) (2023)
- [Relational Causal Models with Cycles:Representation and Reasoning](https://mlsanity.com/paper/2202.10706/) (2022)
- [Score-matching-based Structure Learning for Temporal Data on Networks](https://mlsanity.com/paper/2412.07469/) (2024)
- [Relational Causal Discovery with Latent Confounders](https://mlsanity.com/paper/2507.01700/) (2025)
- [Non-Parametric Inference of Relational Dependence](https://mlsanity.com/paper/2207.00163/) (2022)
- [ORCA: ORchestrating Causal Agent](https://mlsanity.com/paper/2508.21304/) (2025)
- [Compositional Models for Estimating Causal Effects](https://mlsanity.com/paper/2406.17714/) (2024)
- [Partially Observed Structural Causal Models](https://mlsanity.com/paper/2605.03268/) (2026)
- [Lifted Causal Inference](https://mlsanity.com/paper/2606.28024/) (2026)

## Abstract (arXiv)

The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks. Recent developments in the theory and representation of relational models support lifted reasoning about conditional independence. This enables a powerful constraint for orienting bivariate dependencies and forms the basis of a new algorithm for learning structure. We present the relational causal discovery (RCD) algorithm that learns causal relational models. We prove that RCD is sound and complete, and we present empirical results that demonstrate effectiveness.