mlsanityData to 7 Oct 2026

A Fast Non-parametric Approach for Local Causal Structure Learning

arXiv 2111.14969 ↗First posted 29 Nov 2021math.ST stat.ME stat.MLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 46 references that are in the map. First posted on arXiv 29 Nov 2021.

Builds on

14 of 46 references.

References · 14

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  3. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  4. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
  5. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  6. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  7. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
  8. 2019A simple measure of conditional dependencecited by 38
  9. 2020A polynomial-time algorithm for learning nonparametric causal graphscited by 29
  10. 2013Cyclic Causal Discovery from Continuous Equilibrium Datacited by 21
  11. 2021Efficient Bayesian network structure learning via local Markov boundary searchcited by 14
  12. 2021Structure Learning for Directed Treescited by 9
2 more
  1. 2013Score-based Causal Learning in Additive Noise Modelscited by 8
  2. 2013SparsityBoost: A New Scoring Function for Learning Bayesian Network Structurecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  2. 2024Optimal estimation of Gaussian (poly)treescited by 3
  3. 2023Learning Linear Gaussian Polytree Models with Interventionscited by 0
  4. 2025Optimal structure learning and conditional independence testingcited by 0
  5. 2026Detecting Changes in Causal Dependence with Kernels and Copulascited by 0