Work

Budgeted Experiment Design for Causal Structure Learning

Year: 2017

Type: preprint

Abstract: We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$. 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 $(1-\frac{1}{e})$-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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1709.03625

Authors: Ghassami, AmirEmad, Salehkaleybar, Saber, Kiyavash, Negar, Bareinboim, Elias

Institutions: University of Illinois Urbana-Champaign, Sharif University of Technology, Purdue University West Lafayette

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: CAREER: Approximate Causal Inference, RI: Medium: Collaborative Research: Causal Inference: Identification, Learning, and Decision-Making, G6671297155

OpenAlex ID: W2754620186

Cites:

Cited by: 15

Related to: 20