# The Recovery of Causal Poly-Trees from Statistical Data

arXiv: https://arxiv.org/abs/1304.2736 · HTML: https://mlsanity.com/paper/1304.2736/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

## Built on by (most cited first, 19 of 19)

- [Efficient Bayesian network structure learning via local Markov boundary search](https://mlsanity.com/paper/2110.06082/) (2021)
- [Causal reasoning in typical computer vision tasks](https://mlsanity.com/paper/2307.13992/) (2023)
- [Structure Learning for Directed Trees](https://mlsanity.com/paper/2108.08871/) (2021)
- [Learning Linear Non-Gaussian Polytree Models](https://mlsanity.com/paper/2208.06701/) (2022)
- [Learning bounded-degree polytrees with known skeleton](https://mlsanity.com/paper/2310.06333/) (2023)
- [Bridging Weighted First Order Model Counting and Graph Polynomials](https://mlsanity.com/paper/2407.11877/) (2024)
- [Optimal estimation of Gaussian (poly)trees](https://mlsanity.com/paper/2402.06380/) (2024)
- [Tensor tree learns hidden relational structures in data to construct generative models](https://mlsanity.com/paper/2408.10669/) (2024)
- [Possible Principles for Aligned Structure Learning Agents](https://mlsanity.com/paper/2410.00258/) (2024)
- [Greedy equivalence search for nonparametric graphical models](https://mlsanity.com/paper/2406.17228/) (2024)
- [Learning Linear Polytree Structural Equation Models](https://mlsanity.com/paper/2107.10955/) (2021)
- [Causal Identification with Additive Noise Models: Quantifying the Effect of Noise](https://mlsanity.com/paper/2110.08087/) (2021)
- [Navigating causal deep learning](https://mlsanity.com/paper/2212.00911/) (2022)
- [Distribution Learning Meets Graph Structure Sampling](https://mlsanity.com/paper/2405.07914/) (2024)
- [The Effect of Noise Level on Causal Identification with Additive Noise Models](https://mlsanity.com/paper/2108.11320/) (2021)
- [Estimating large causal polytrees from small samples](https://mlsanity.com/paper/2209.07028/) (2022)
- [A Comprehensively Improved Hybrid Algorithm for Learning Bayesian Networks: Multiple Compound Memory Erasing](https://mlsanity.com/paper/2212.03103/) (2022)
- [Learning Linear Gaussian Polytree Models with Interventions](https://mlsanity.com/paper/2311.04636/) (2023)
- [Optimal structure learning and conditional independence testing](https://mlsanity.com/paper/2507.05689/) (2025)

## Abstract (arXiv)

Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering ply-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a ply-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.