Year: 2018
Type: preprint
Abstract: We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove that we can achieve a constant factor approximation to this problem with a greedy algorithm. We then constrain the sparsity of each intervention. We develop an algorithm that returns an intervention design that is nearly optimal in terms of size for sparse graphs with sparse interventions and we discuss how to use it when there are costs on the vertices.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3424441068, CIF: Medium: Collaborative Research: Coded Computing for Large-Scale Machine Learning, CIF: Small: Index Coding and Matrix Factorizations, CIF: Small: Sparsity in Quadratic Optimization through Low-Rank Approximations, G7452299184, Collaborative Research: Connecting Submodularity and Restricted Strong Convexity, Graduate Research Fellowship Program
OpenAlex ID: W2889973941