# Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments

arXiv: https://arxiv.org/abs/1702.08567 · HTML: https://mlsanity.com/paper/1702.08567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Adaptive Online Experimental Design for Causal Discovery](https://mlsanity.com/paper/2405.11548/) (2024)
- [Score-Based Learning of Cluster DAGs from Interventions](https://mlsanity.com/paper/2610.11947/) (2026)

## Abstract (arXiv)

We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most M experiments in a non-adaptive manner. We consider the optimal learning strategy in terms of minimizing the portions of the structure that remains unknown given the limited number of experiments in both Bayesian and minimax setting. We characterize the theoretical optimal solution and propose an algorithm, which designs the experiments efficiently in terms of time complexity. We show that for bounded degree graphs, in the minimax case and in the Bayesian case with uniform priors, our proposed algorithm is a ρ-approximation algorithm, where ρ is independent of the order of the underlying graph. Simulations on both synthetic and real data show that the performance of our algorithm is very close to the optimal solution.