# Probability trees and the value of a single intervention

arXiv: https://arxiv.org/abs/2205.08779 · HTML: https://mlsanity.com/paper/2205.08779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 4 of 4)

- [ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery](https://mlsanity.com/paper/1902.10347/) (2019)
- [Entropic Causal Inference: Identifiability and Finite Sample Results](https://mlsanity.com/paper/2101.03501/) (2021)
- [Algorithms for Causal Reasoning in Probability Trees](https://mlsanity.com/paper/2010.12237/) (2020)
- [Subjectivity, Bayesianism, and Causality](https://mlsanity.com/paper/1407.4139/) (2014)

## Abstract (arXiv)

The most fundamental problem in statistical causality is determining causal relationships from limited data. Probability trees, which combine prior causal structures with Bayesian updates, have been suggested as a possible solution. In this work, we quantify the information gain from a single intervention and show that both the anticipated information gain, prior to making an intervention, and the expected gain from an intervention have simple expressions. This results in an active-learning method that simply selects the intervention with the highest anticipated gain, which we illustrate through several examples. Our work demonstrates how probability trees, and Bayesian estimation of their parameters, offer a simple yet viable approach to fast causal induction.