Budgeted Experiment Design for Causal Structure Learning
Abstract
We study the problem of causal structure learning when the experimenter is limited to perform at most non-adaptive experiments of size . We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolved. We prove that the corresponding objective function is submodular and a greedy algorithm suffices to achieve -approximation of the optimal value. We further present an accelerated variant of the greedy algorithm, which can lead to orders of magnitude performance speedup. We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions.
Cite
@article{arxiv.1709.03625,
title = {Budgeted Experiment Design for Causal Structure Learning},
author = {AmirEmad Ghassami and Saber Salehkaleybar and Negar Kiyavash and Elias Bareinboim},
journal= {arXiv preprint arXiv:1709.03625},
year = {2018}
}