# Context-Specific Causal Discovery for Categorical Data Using Staged Trees

arXiv: https://arxiv.org/abs/2106.04416 · HTML: https://mlsanity.com/paper/2106.04416/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Causal Networks: Semantics and Expressiveness](https://mlsanity.com/paper/1304.2379/) (2013)
- [Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA](https://mlsanity.com/paper/1904.09096/) (2019)
- [Differentiable Causal Discovery Under Unmeasured Confounding](https://mlsanity.com/paper/2010.06978/) (2020)
- [Context-Specific Independence in Bayesian Networks](https://mlsanity.com/paper/1302.3562/) (2013)
- [Towards Scalable Bayesian Learning of Causal DAGs](https://mlsanity.com/paper/2010.00684/) (2020)
- [Compositional Abstraction Error and a Category of Causal Models](https://mlsanity.com/paper/2103.15758/) (2021)
- [The R Package stagedtrees for Structural Learning of Stratified Staged Trees](https://mlsanity.com/paper/2004.06459/) (2020)
- [A Bayesian Approach to Learning Bayesian Networks with Local Structure](https://mlsanity.com/paper/1302.1528/) (2013)
- [Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models](https://mlsanity.com/paper/1310.1187/) (2013)
- [The curved exponential family of a staged tree](https://mlsanity.com/paper/2010.15515/) (2020)
- [Algorithms for Causal Reasoning in Probability Trees](https://mlsanity.com/paper/2010.12237/) (2020)
- [Sensitivity analysis beyond linearity](https://mlsanity.com/paper/1901.02062/) (2018)
- [Recovering Causal Structures from Low-Order Conditional Independencies](https://mlsanity.com/paper/2010.02675/) (2020)
- [Learning from Pairwise Marginal Independencies](https://mlsanity.com/paper/1508.00280/) (2015)
- [Algebraic geometry of discrete interventional models](https://mlsanity.com/paper/2012.03593/) (2020)
- [Equivalence Classes of Staged Trees](https://mlsanity.com/paper/1512.00209/) (2015)

## Built on by (most cited first, 10 of 10)

- [Staged trees and asymmetry-labeled DAGs](https://mlsanity.com/paper/2108.01994/) (2021)
- [Structural Learning of Simple Staged Trees](https://mlsanity.com/paper/2203.04390/) (2022)
- [Highly Efficient Structural Learning of Sparse Staged Trees](https://mlsanity.com/paper/2206.06970/) (2022)
- [Automated Discovery of Functional Actual Causes in Complex Environments](https://mlsanity.com/paper/2404.10883/) (2024)
- [Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear](https://mlsanity.com/paper/2301.00629/) (2023)
- [OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework](https://mlsanity.com/paper/2406.04598/) (2024)
- [Learning Staged Trees from Incomplete Data](https://mlsanity.com/paper/2405.18306/) (2024)
- [Scalable Structure Learning for Sparse Context-Specific Systems](https://mlsanity.com/paper/2402.07762/) (2024)
- [Context-Specific Refinements of Bayesian Network Classifiers](https://mlsanity.com/paper/2405.18298/) (2024)
- [Staged Event Trees for Transparent Treatment Effect Estimation](https://mlsanity.com/paper/2509.26265/) (2025)

## Abstract (arXiv)

Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can represent complex and asymmetric causal relationships between categorical variables. We provide a first graphical representation of the equivalence class of a staged tree, by looking only at a specific subset of its underlying independences. We further define a new pre-metric, inspired by the widely used structural intervention distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complexes, asymmetric causal relationships from data, and real-world data applications illustrate their use in practical causal analysis.