{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantile-causal-discovery","title":"Quantile Causal Discovery","arxiv_id":null,"date":"2020-01-01","proceeding":"ICML 2020 1","authors":["Natasa Tagasovska","Thibault Vatter","Valérie Chavez-Demoulin"],"abstract":"Causal inference using observational data is challenging, especially in the bivariate case.\nThrough the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression.\nBased on this theory, we develop Quantile Causal Discovery (QCD), a new method to uncover causal relationships.\nBecause it uses multiple quantile levels instead of the conditional mean only, QCD is adaptive not only to additive, but also to multiplicative or even location-scale generating mechanisms.\nTo illustrate the empirical effectiveness of our approach, we perform an extensive empirical comparison on both synthetic and real datasets.\nThis study shows that QCD is robust across different implementations of the method (i.e., the quantile regression algorithm), computationally efficient, and compares favorably to state-of-the-art methods.","url_abs":"https://proceedings.icml.cc/static/paper_files/icml/2020/3826-Paper.pdf","url_pdf":"https://proceedings.icml.cc/static/paper_files/icml/2020/3826-Paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quantile-causal-discovery","repo_url":"https://github.com/tagas/bQCD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"},{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"quantile-regression","task_name":"quantile regression"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}