# Algorithms for Causal Reasoning in Probability Trees

arXiv: https://arxiv.org/abs/2010.12237 · HTML: https://mlsanity.com/paper/2010.12237/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Context-Specific Causal Discovery for Categorical Data Using Staged Trees](https://mlsanity.com/paper/2106.04416/) (2021)
- [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)
- [Assisting clinical practice with fuzzy probabilistic decision trees](https://mlsanity.com/paper/2304.07788/) (2023)
- [Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data](https://mlsanity.com/paper/2501.15973/) (2025)
- [A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference](https://mlsanity.com/paper/2503.17803/) (2025)
- [Active learning of causal probability trees](https://mlsanity.com/paper/2205.08178/) (2022)
- [Probability trees and the value of a single intervention](https://mlsanity.com/paper/2205.08779/) (2022)

## Abstract (arXiv)

Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community. Here we present concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events. Our work expands the domain of causal reasoning to a very general class of discrete stochastic processes.