# Ancestral Causal Inference

arXiv: https://arxiv.org/abs/1606.07035 · HTML: https://mlsanity.com/paper/1606.07035/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2016.

## Built on by (most cited first, 24 of 24)

- [A Survey on Causal Discovery: Theory and Practice](https://mlsanity.com/paper/2305.10032/) (2023)
- [BaCaDI: Bayesian Causal Discovery with Unknown Interventions](https://mlsanity.com/paper/2206.01665/) (2022)
- [Causal Discovery with Fewer Conditional Independence Tests](https://mlsanity.com/paper/2406.01823/) (2024)
- [Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions](https://mlsanity.com/paper/2211.14897/) (2022)
- [Local Causal Discovery for Estimating Causal Effects](https://mlsanity.com/paper/2302.08070/) (2023)
- [When Selection Meets Intervention: Additional Complexities in Causal Discovery](https://mlsanity.com/paper/2503.07302/) (2025)
- [Expert-Aided Causal Discovery of Ancestral Graphs](https://mlsanity.com/paper/2309.12032/) (2023)
- [Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders](https://mlsanity.com/paper/2501.10124/) (2025)
- [A Local Method for Identifying Causal Relations under Markov Equivalence](https://mlsanity.com/paper/2102.12685/) (2021)
- [Causal discovery under a confounder blanket](https://mlsanity.com/paper/2205.05715/) (2022)
- [Large-Scale Targeted Cause Discovery via Learning from Simulated Data](https://mlsanity.com/paper/2408.16218/) (2024)
- [Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning](https://mlsanity.com/paper/2603.04780/) (2026)
- [Learning to Defer for Causal Discovery with Imperfect Experts](https://mlsanity.com/paper/2502.13132/) (2025)
- [Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data](https://mlsanity.com/paper/2509.25800/) (2025)
- [Definite Non-Ancestral Relations and Structure Learning](https://mlsanity.com/paper/2105.10350/) (2021)
- [Hybrid Bayesian network discovery with latent variables by scoring multiple interventions](https://mlsanity.com/paper/2112.10574/) (2021)
- [Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders](https://mlsanity.com/paper/2401.13009/) (2024)
- [dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data](https://mlsanity.com/paper/2505.06542/) (2025)
- [Multi-omic Causal Discovery using Genotypes and Gene Expression](https://mlsanity.com/paper/2505.15866/) (2025)
- [On the Number of Conditional Independence Tests in Constraint-based Causal Discovery](https://mlsanity.com/paper/2603.21844/) (2026)
- [Causal Bayesian Optimization with Unknown Graphs](https://mlsanity.com/paper/2503.19554/) (2025)
- [A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs](https://mlsanity.com/paper/2605.06288/) (2026)
- [Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data](https://mlsanity.com/paper/2607.04527/) (2026)
- [Integrating Background Knowledge for Scalable Causal Discovery](https://mlsanity.com/paper/2607.10456/) (2026)

## Abstract (arXiv)

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.