A Fast Non-parametric Approach for Local Causal Structure Learning

2021/11/29 by Mona Azadkia, Azadkia, Mona, Armeen Taeb +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #62D20 #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Gene expression and cancer classification #Machine Learning (stat.ML) #Machine Learning and Algorithms #Methodology (stat.ME) #Statistics Theory (math.ST) #math.ST #msc:62D20 #stat.ME #stat.ML #stat.TH

paper · pdf · doi:10.48550/arxiv.2111.14969

27 pages

openalex publication_date 2021/11/29 · arxiv created 2022/03/18 · arxiv updated 2022/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study the problem of causal structure learning with essentially no assumptions on the functional relationships and noise. We develop DAG-FOCI, a computationally fast algorithm for this setting that is based on the FOCI variable selection algorithm in~\citeazadkia2021simple. DAG-FOCI outputs the set of parents of a response variable of interest. We provide theoretical guarantees of our procedure when the underlying graph does not contain any (undirected) cycle containing the response variable of interest. Furthermore, in the absence of this assumption, we give a conservative guarantee against false positive causal claims when the set of parents is identifiable. We demonstrate the applicability of DAG-FOCI on simulated as well as a real dataset from computational biology~\citesachs2005causal.

Cited by

• Optimal structure learning and conditional independence testing

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