mlsanityData to 13 Oct 2026

Causal Inference via Kernel Deviance Measures

arXiv 1804.04622 ↗First posted 12 Apr 2018cs.LG stat.ME stat.MLcited by 22

Not on the map. Why?

Abstract (arXiv)

Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery method based on purely observational data. From a novel interpretation of the notion of asymmetry between cause and effect, we derive a corresponding asymmetry measure using the framework of reproducing kernel Hilbert spaces. Based on this, we propose three decision rules for causal discovery. We demonstrate the wide applicability of our method across a range of diverse synthetic datasets. Furthermore, we test our method on real-world time series data and the real-world benchmark dataset Tubingen Cause-Effect Pairs where we outperform existing state-of-the-art methods.

Key facts. Cited by 22 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Apr 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2019: 1; 2020: 0; 2021: 4; 2022: 6; 2023: 2; 2024: 4; 2025: 2; 2026: 3.

22 papers.

Sort:

By area:

  1. 2019Causal Confusion in Imitation Learningcited by 287
  2. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 210
  3. 2024Robust agents learn causal world modelscited by 81
  4. 2022Learning to Induce Causal Structurecited by 68
  5. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  6. 2022Optimal Rates for Regularized Conditional Mean Embedding Learningcited by 50
  7. 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
  8. 2021Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processescited by 27
  9. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  10. 2021BayesIMP: Uncertainty Quantification for Causal Data Fusioncited by 21
  11. 2023Bivariate Causal Discovery using Bayesian Model Selectioncited by 12
  12. 2021On the Sample Complexity of Causal Discovery and the Value of Domain Expertisecited by 9
10 more
  1. 2024A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discoverycited by 7
  2. 2025Measure-Theoretic Anti-Causal Representation Learningcited by 4
  3. 2023Causal Discovery via Conditional Independence Testing with Proxy Variablescited by 3
  4. 2025Amortized Linear-time Exact Shapley Value for Product-Kernel Methodscited by 3
  5. 2026Topological Residual Asymmetry for Bivariate Causal Directioncited by 2
  6. 2026Structure-agnostic Causal Representation Learningcited by 1
  7. 2022Variation-based Cause Effect Identificationcited by 0
  8. 2024Causal Discovery by Kernel Deviance Measures with Heterogeneous Transformscited by 0
  9. 2024Scalable Signature-Based Distribution Regression via Reference Setscited by 0
  10. 2026Functional Causal Discovery via Conditional Covariance Orderingcited by 0