{"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/kh-spn-characteristic-interventional-sum","title":"$χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains","arxiv_id":"2408.07545","date":"2024-08-14","proceeding":null,"authors":["Harsh Poonia","Moritz Willig","Zhongjie Yu","Matej Zečević","Kristian Kersting","Devendra Singh Dhami"],"abstract":"Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ($\\chi$SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. $\\chi$SPN uses characteristic functions in the leaves of an interventional SPN (iSPN) thereby providing a unified view for discrete and continuous random variables through the Fourier-Stieltjes transform of the probability measures. A neural network is used to estimate the parameters of the learned iSPN using the intervened data. Our experiments on 3 synthetic heterogeneous datasets suggest that $\\chi$SPN can effectively capture the interventional distributions for both discrete and continuous variables while being expressive and causally adequate. We also show that $\\chi$SPN generalize to multiple interventions while being trained only on a single intervention data.","url_abs":"https://arxiv.org/abs/2408.07545v1","url_pdf":"https://arxiv.org/pdf/2408.07545v1.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":"kh-spn-characteristic-interventional-sum","repo_url":"https://github.com/harpoonix/chi-spn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[{"method_slug":"characteristic-functions","method_name":"Characteristic Functions"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}