[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85219-en":3,"doc-seo-85219-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85219,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Integrating Background Knowledge for Scalable Causal Discovery","Expert background knowledge is often available in practical causal discovery applications, where constraints on the true causal graph improve identifiability and accuracy while shrinking the set of candidate graphs. Yet many existing approaches inject this knowledge only after structure learning via postprocessing. This work introduces a framework that uses background knowledge throughout causal discovery, targeting scalable methods that recover only graph subsets. Experiments across multiple algorithms show reduced computational requirements and improved structure quality for learned causal relations.","Integrating Background Knowledge for Scalable  \nCausal Discovery  \nMtys Schubert  \nUniversity of Amsterdam  \nTheofanis Aslanidis Tom Claassen  \nUniversity of Amsterdam Radboud University Nijmegen  \narXiv :2607 . 10456v1 [ stat .ML] 11 Jul 2026  \nSara Magliacane  \nSaarland University  \nUniversity of Amsterdam  \nAbstract  \nExpert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure, but also in reducing the space of candidate causal graphs. As causal discovery can become computationally expensive for large number of variables, it is crucial to utilize background knowledge effectively during the causal discovery process.  \nHowever, most current methods only use background knowledge in a postprocessing step after causal discovery to refine the learned graph. In this work, we develop a framework for utilizing background knowledge during the causal discovery process, focusing especially on scalable causal discovery methods that recover only a subset of the whole graph. We implement our framework for multiple algorithms and empirically show that utilizing background knowledge can both reduce computational requirements and increase the quality of the learned structures.  \n1 Introduction  \nCausal graphs are a popular tool to represent the causal data generating process underlying observations [1] . Causal discovery aims to identify causal relations between causal variables by identifying (parts of) the causal graph from data [2] . These causal relations can then be used for causal effect estimation [3], to deepen our understanding of natural systems [4] and for policy-making [5] . However, causal discovery can become computationally demanding when applied to high-dimensional settings, which can limit its applicability in practical problems.  \nOne way to address this issue is to adapt the objective of causal discovery and focus on the downstream task for which the learned causal relations will be used. For example, if the goal is to estimate the causal effect of a treatment on an outcome, then a large portion of the causal graph may be irrelevant. Motivated by this, several scalable causal discovery methods focus on learning only a set of relevant causal relations for a given causal inference task [6, 7, 8] . A popular approach to achieve this is local causal discovery, which identifies only the local structure of a target variable [9, 10, 11] . While these methods improve the computational efficiency and hence applicability of causal discovery methods to hundreds and thousands of variables, they rarely allow for the integration of expert knowledge. In many real-world settings, experts may have background knowledge on the presence or lack of causal relations between variables, e.g., in the form of direct [12] or ancestral relations [13], or tiers arising in temporal studies [14] . Background knowledge can also be extracted from experiments [15, 16], or even large language models [17, 18] . Such background knowledge can improve the accuracy and identifiability of learned causal relations [19, 20, 21] . However, this knowledge is often only applied a posteriori after causal discovery, and is not leveraged to reduce the search space  \nPreprint.  \nand improve the scalability of causal discovery. Logic-based methods allow for rich background knowledge in the form of logic rules and its seamless integration in the search process, but can only handle tens of variables [22, 23, 24] .  \nIn this work we develop methods to utilize background knowledge during causal discovery, to improve not only the accuracy and identifiability of the learned causal relations, but also the scalability of current methods. We focus on background knowledge about direct, or pairwise, causal relations, i.e., the existence and orientation of specific edges in the true causal graph, and s","cbCailONWjBPpMNy","https://ap.wps.com/l/cbCailONWjBPpMNy","pdf",1829897,2,1,45,"English","en",105,"# Introduction\n# Background","[{\"question\":\"Why is background knowledge important in causal discovery?\",\"answer\":\"Background knowledge constrains the true causal graph, which improves identifiability of causal effects and accuracy of learned structures. It also reduces the space of candidate causal graphs.\"},{\"question\":\"What limitation do the authors identify in most current methods?\",\"answer\":\"Most current approaches apply background knowledge only in a postprocessing step after causal discovery, rather than using it to directly reduce the search space and improve scalability during learning.\"},{\"question\":\"How does the proposed framework improve scalability and results?\",\"answer\":\"The framework integrates background knowledge during causal discovery, especially for scalable methods that recover only a subset of the full graph. It is implemented for global and local algorithms, where the extensions are sound for propagating knowledge and (except LDECC) complete, and is evaluated using time/CI tests and causal-effect accuracy.\"}]",1784201814,113,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-background-knowledge-for-scalable-causal-discovery","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/integrating-background-knowledge-for-scalable-causal-discovery/85219/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is background knowledge important in causal discovery?","Question",{"text":75,"@type":76},"Background knowledge constrains the true causal graph, which improves identifiability of causal effects and accuracy of learned structures. It also reduces the space of candidate causal graphs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation do the authors identify in most current methods?",{"text":80,"@type":76},"Most current approaches apply background knowledge only in a postprocessing step after causal discovery, rather than using it to directly reduce the search space and improve scalability during learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework improve scalability and results?",{"text":84,"@type":76},"The framework integrates background knowledge during causal discovery, especially for scalable methods that recover only a subset of the full graph. It is implemented for global and local algorithms, where the extensions are sound for propagating knowledge and (except LDECC) complete, and is evaluated using time/CI tests and causal-effect accuracy.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]