# Probabilistic Active Learning of Functions in Structural Causal Models

arXiv: https://arxiv.org/abs/1706.10234 · HTML: https://mlsanity.com/paper/1706.10234/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Active Bayesian Causal Inference](https://mlsanity.com/paper/2206.02063/) (2022)
- [Active Learning of Continuous-time Bayesian Networks through Interventions](https://mlsanity.com/paper/2105.14742/) (2021)
- [Online Identification of IT Systems through Active Causal Learning](https://mlsanity.com/paper/2509.02130/) (2025)

## Abstract (arXiv)

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic active learning scheme that identifies the intervention which is optimally informative about all of the unknown functions jointly, given previously observed data. We test the derived algorithms on simple examples, to demonstrate that they produce a structured exploration policy that significantly improves on unstructured base-lines.