ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2018
Generalized Score Functions for Causal Discovery.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Are we ready for causal discovery in biological systems using deep learning?Briefings in bioinformatics · 2026Article
- Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R.Behavior research methods · 2026Review
- A large-scale benchmark for network inference from single-cell perturbation data.Communications biology · 2025Article
- Causal Inference for Hypertension Prediction With Wearable E lectrocardiogram and P hotoplethysmogram Signals: Feasibility Study.JMIR cardio · 2025Article
- Cause-effect perception in an object place task.Frontiers in cognition · 2025Article
- Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals.Scientific reports · 2024Article
- Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.Medical physics · 2024Article
- Article
- Deep causal learning for robotic intelligence.Frontiers in neurorobotics · 2023Review
- Disentangling personalized treatment effects from "time-of-the-day" confounding in mobile health studies.PloS one · 2022Article
- Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.Proceedings of machine learning research · 2019Article
- Review of Causal Discovery Methods Based on Graphical Models.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a non-parametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.