mlsanityData to 7 Oct 2026

Telling Cause from Effect using MDL-based Local and Global Regression

arXiv 1709.08915 ↗First posted 26 Sep 2017stat.MLcited by 21

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Abstract (arXiv)

We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables X and Y from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity and use the Minimum Description Length (MDL) principle to provide a practical solution. In particular, we propose a compression scheme to encode local and global functional relations using MDL-based regression. We infer X causes Y in case it is shorter to describe Y as a function of X than the inverse direction. In addition, we introduce Slope, an efficient linear-time algorithm that through thorough empirical evaluation on both synthetic and real world data we show outperforms the state of the art by a wide margin.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2017.

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Citing papers per year: 2021: 1; 2022: 5; 2023: 5; 2024: 0; 2025: 6; 2026: 4.

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  1. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  2. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  3. 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
  4. 2022Causal Discovery in Hawkes Processes by Minimum Description Lengthcited by 11
  5. 2021Causal versions of Maximum Entropy and Principle of Insufficient Reasoncited by 7
  6. 2023Robust Estimation of Causal Heteroscedastic Noise Modelscited by 6
  7. 2022Ordinal Causal Discoverycited by 5
  8. 2023Structural restrictions in local causal discovery: identifying direct causes of a target variablecited by 5
  9. 2022Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutationcited by 3
  10. 2023Reinterpreting causal discovery as the task of predicting unobserved joint statisticscited by 3
  11. 2025Identifying Causal Direction via Variational Bayesian Compressioncited by 3
  12. 2025Identifying Causal Direction via Dense Functional Classescited by 3
9 more
  1. 2025Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Datacited by 1
  2. 2025Skewness-Robust Causal Discovery in Location-Scale Noise Modelscited by 1
  3. 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
  4. 2022Variation-based Cause Effect Identificationcited by 0
  5. 2025Hybrid Causal Identification and Causal Mechanism Clusteringcited by 0
  6. 2025LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classescited by 0
  7. 2026One Ruler: A Same-Hands Re-Evaluation of Bivariate Causal Direction on Tuebingen, with a Parameter-Free Compression Baselinecited by 0
  8. 2026MDL Meets Latent Confounders: LNML-based Causal Discoverycited by 0
  9. 2026Inferring Causal Relations between Two Sequences of Events with Language Modelscited by 0