# Causal Discovery by Interventions via Integer Programming

arXiv: https://arxiv.org/abs/2412.01674 · HTML: https://mlsanity.com/paper/2412.01674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Adaptivity Complexity for Causal Graph Discovery](https://mlsanity.com/paper/2306.05781/) (2023)
- [Adaptive Online Experimental Design for Causal Discovery](https://mlsanity.com/paper/2405.11548/) (2024)
- [New metrics and search algorithms for weighted causal DAGs](https://mlsanity.com/paper/2305.04445/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Design](https://mlsanity.com/paper/2504.03122/) (2025)

## Abstract (arXiv)

Causal discovery is essential across various scientific fields to uncover causal structures within data. Traditional methods relying on observational data have limitations due to confounding variables. This paper presents an optimization-based approach using integer programming (IP) to design minimal intervention sets that ensure causal structure identifiability. Our method provides exact and modular solutions that can be adjusted to different experimental settings and constraints. We demonstrate its effectiveness through comparative analysis across different settings, demonstrating its applicability and robustness.