Conditional distribution variability measures for causality detection
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Abstract (arXiv)
In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jan 2016.
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Citing papers per year: 2021: 3; 2022: 6; 2023: 1; 2024: 1; 2025: 3; 2026: 1.
15 papers.
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- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
- 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
- 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
- 2023Bivariate Causal Discovery using Bayesian Model Selectioncited by 12
- 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
- 2022Unifying Pairwise Interactions in Complex Dynamicscited by 8
- 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 8
- 2025Distinguishing Cause from Effect with Causal Velocity Modelscited by 6
- 2022Do learned representations respect causal relationships?cited by 3
- 2022Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertaintycited by 2
- 2022From Causal Pairs to Causal Graphscited by 1
- 2022Initial Results for Pairwise Causal Discovery Using Quantitative Information Flowcited by 1
3 more
- 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
- 2025From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discoverycited by 0
- 2025LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classescited by 0
- 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach7 Apr 2026
- 2025LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classes10 Dec 2025
- 2025From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery27 Jul 2025
- 2025Distinguishing Cause from Effect with Causal Velocity Models7 Feb 2025
- 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework12 Jun 2024
- 2023Bivariate Causal Discovery using Bayesian Model Selection5 Jun 2023
- 2022Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow2 Dec 2022
- 2022From Causal Pairs to Causal Graphs8 Nov 2022
- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Models13 Oct 2022
- 2022Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty25 Jun 2022
- 2022Do learned representations respect causal relationships?2 Apr 2022
- 2022Unifying Pairwise Interactions in Complex Dynamics28 Jan 2022
3 more
- 2021ML4C: Seeing Causality Through Latent Vicinity1 Oct 2021
- 2021Model-Based Counterfactual Synthesizer for Interpretation16 Jun 2021
- 2021Nonlinear Invariant Risk Minimization: A Causal Approach24 Feb 2021
- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
- 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
- 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
- 2023Bivariate Causal Discovery using Bayesian Model Selectioncited by 12
- 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
- 2022Unifying Pairwise Interactions in Complex Dynamicscited by 8
- 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 8
- 2025Distinguishing Cause from Effect with Causal Velocity Modelscited by 6
- 2022Do learned representations respect causal relationships?cited by 3
- 2022Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertaintycited by 2
- 2022From Causal Pairs to Causal Graphscited by 1
- 2022Initial Results for Pairwise Causal Discovery Using Quantitative Information Flowcited by 1
12 most cited of 15 All 15 (JSON)
- 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach7 Apr 2026
- 2025LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classes10 Dec 2025
- 2025From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery27 Jul 2025
- 2025Distinguishing Cause from Effect with Causal Velocity Models7 Feb 2025
- 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework12 Jun 2024
- 2023Bivariate Causal Discovery using Bayesian Model Selection5 Jun 2023
- 2022Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow2 Dec 2022
- 2022From Causal Pairs to Causal Graphs8 Nov 2022
- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Models13 Oct 2022
- 2022Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty25 Jun 2022
- 2022Do learned representations respect causal relationships?2 Apr 2022
- 2022Unifying Pairwise Interactions in Complex Dynamics28 Jan 2022
12 newest of 15 All 15 (JSON)