[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86330-en":3,"doc-seo-86330-105":30,"detail-sidebar-cat-0-en-105":83},{"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},86330,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Relaxing Faithfulness with Intervention-Only Causal Discovery","Causal discovery algorithms learn directed causal structures among random variables from data. Standard workflows rely on conditional independence to partially orient a graph, then use interventions to resolve remaining directions, under the faithfulness assumption that causal links induce statistical dependence. Many systems exhibit buffering pathways whose cancellations break faithfulness and cause algorithms to miss true dependencies. This paper shows that hard interventions provide the missing linkage information, and introduces intervention-immediacy faithfulness to nonparametrically identify causal structure.","Relaxing Faithfulness with Intervention-Only Causal Discovery  \nBijan H. S. Mazaheri 1,2 Jiaqi Zhang2, 3 Caroline Uhler2, 3  \n1Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA  \n2 Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA  \n3Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA  \narXiv :2607 . 1 18 16v 1 [ cs .LG] 13 Jul 2026  \nAbstract  \nCausal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption—called intervention-immediacy faithfulness  \n—that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventionsas the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.  \n1 INTRODUCTION  \nCausal Discovery. Structural Causal Models (SCMs), popularized by the works of Pearl [1998, 2009], graphically describe networks of causal dependencies. Causal discovery is the task of recovering the underlying causal structure from data, typically in the form of a directed acyclic graph  \n(DAG) or a representation of an equivalence class of DAGs, though other targets, such as ancestral graphs with latent variables or cyclic models, are also studied (see Vowelset al. [2022], Squires and Uhler [2023] for reviews) . One approach to causal discovery involves a constraint-based search guided by conditional independence (“CI-tests”), e.g., the PC-algorithm from Spirtes et al. [2000] . Constraintbased approaches use the observation that variables without a direct causal link can be made independent by conditioning on variables on intermediary causal paths, known as the causal Markov condition [Pearl, 2009] .  \nStructural causal models imply a data-generating process whereby each random variable is generated from its (causal) parents. These processes are sometimes specified using equations, leading to structural equation models (SEMs) . Under these settings, these equations may be restricted using “parametric assumptions” on their functional form and noise. The most popular such assumption is linearity with additive Gaussian noise, yielding a multivariate Gaussian distribution on the full system.  \nCausal discovery algorithms therefore fall under two categories: (1) algorithms that make use of parametric assumptions on the structural equations, and (2) non-parametric algorithms that only make use of conditional independence and other broad distributional properties. While parametric settings, such as Linear Non-Gaussian Additive Noise Models (LinGAMs) [Shimizu et al., 2006], can sometimes fully recover a single DAG structure, most non-parametric algorithms for causal discovery can only narrow the structure down to what is known as a “Markov equivalence class,” uniquely specified by its conditional independence constraints [Spirtes et al., 2000] . Such a class is usually represented ","cbCaie5Mcd9fdBGP","https://ap.wps.com/l/cbCaie5Mcd9fdBGP","pdf",457992,4,1,16,"English","en",105,"# Abstract\n# Introduction\n## Causal Discovery\n## Interventions\n## Faithfulness","[{\"question\":\"What does the paper do when intervention scope is limited?\",\"answer\":\"It specifies equivalence classes for causal structures when identification criteria are not met due to constraints on which interventions are available.\"}]",1784210509,40,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"relaxing-faithfulness-with-intervention-only-causal-discovery","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/relaxing-faithfulness-with-intervention-only-causal-discovery/86330/",{"url":52,"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","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What does the paper do when intervention scope is limited?","Question",{"text":75,"@type":76},"It specifies equivalence classes for causal structures when identification criteria are not met due to constraints on which interventions are available.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,111,114,119,122,126],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":29,"slug":110},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]