mlsanityData to 7 Oct 2026

Structural restrictions in local causal discovery: identifying direct causes of a target variable

arXiv 2307.16048 ↗First posted 29 Jul 2023stat.ME math.ST stat.MLcited by 5

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Abstract (arXiv)

We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the causal structure is a fundamental problem in science. Several results are known when the full DAG is identifiable from the distribution, such as assuming a nonlinear Gaussian data-generating process. Here, we are only interested in identifying the direct causes of one target variable (local causal structure), not the full DAG. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms. In contrast to the Invariance Causal Prediction framework, we only assume that we observe one environment without any interventions. We discuss different assumptions for the data-generating process of the target variable under which the set of direct causes is identifiable from the distribution. While doing so, we put essentially no assumptions on the variables other than the target variable. In addition to the novel identifiability results, we provide two practical algorithms for estimating the direct causes from a finite random sample and demonstrate their effectiveness on several benchmark and real datasets.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 79 references that are in the map. First posted on arXiv 29 Jul 2023.

Builds on

22 of 79 references.

References · 22

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  3. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  4. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  5. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  6. 2018Anchor regression: heterogeneous data meets causalitycited by 116
  7. 2020Causal Autoregressive Flowscited by 97
  8. 2017Invariant Causal Prediction for Sequential Datacited by 63
  9. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  10. 2018On Causal Discovery with Equal Variance Assumptioncited by 55
  11. 2016Kernel-based Tests for Joint Independencecited by 40
  12. 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
10 more
  1. 2017Telling Cause from Effect using MDL-based Local and Global Regressioncited by 21
  2. 2017Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)cited by 18
  3. 2014Instrumental Variables: An Econometrician's Perspectivecited by 15
  4. 2021Efficient Bayesian network structure learning via local Markov boundary searchcited by 14
  5. 2013On the Equivalence of Causal Modelscited by 13
  6. 2013Score-based Causal Learning in Additive Noise Modelscited by 8
  7. 2023Nonlinear Causal Discovery with Confounderscited by 8
  8. 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testingcited by 7
  9. 2020A Critical View of the Structural Causal Modelcited by 6
  10. 2023Identifiability of causal graphs under nonadditive conditionally parametric causal modelscited by 6

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025CLEAR: Calibrated Learning for Epistemic and Aleatoric Riskcited by 6
  2. 2024Local Causal Discovery with Background Knowledgecited by 5
  3. 2025Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effectscited by 1
  4. 2026Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approachcited by 1
  5. 2026On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Modelscited by 0