{"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/scalable-and-flexible-causal-discovery-with","title":"Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency","arxiv_id":"2406.09177","date":"2024-06-13","proceeding":null,"authors":["Alan Nawzad Amin","Andrew Gordon Wilson"],"abstract":"To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is enormous so scalably and accurately searching for the best fit to the data is a challenge. In principle we could substantially decrease the search space, or learn the graph entirely, by testing the conditional independence of variables. However, deciding if two variables are adjacent in a causal graph may require an exponential number of tests. Here we build a scalable and flexible method to evaluate if two variables are adjacent in a causal graph, the Differentiable Adjacency Test (DAT). DAT replaces an exponential number of tests with a provably equivalent relaxed problem. It then solves this problem by training two neural networks. We build a graph learning method based on DAT, DAT-Graph, that can also learn from data with interventions. DAT-Graph can learn graphs of 1000 variables with state of the art accuracy. Using the graph learned by DAT-Graph, we also build models that make much more accurate predictions of the effects of interventions on large scale RNA sequencing data.","url_abs":"https://arxiv.org/abs/2406.09177v2","url_pdf":"https://arxiv.org/pdf/2406.09177v2.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":"scalable-and-flexible-causal-discovery-with","repo_url":"https://github.com/alannawzadamin/dat-graph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"},{"task_slug":"graph-learning","task_name":"Graph Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.09177","atlas_url":"https://app.syntology.ai/?focus=2406.09177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09177"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alannawzadamin/dat-graph","reach":{"status":"ok"}}],"summary":{"ran":4},"by_repo_kind":{"official":{"samples":4,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"a729a544891b83f7","entry":"compare_mats","repo":"alannawzadamin/dat-graph","repo_kind":"official","path":"dat_graph/plotting.py","file_url":"https://github.com/alannawzadamin/dat-graph/blob/HEAD/dat_graph/plotting.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a729a544891b83f7"}},{"code_sha256_prefix":"fb2341d14e8b8291","entry":"meeks_rules","repo":"alannawzadamin/dat-graph","repo_kind":"official","path":"dat_graph/graph_ops.py","file_url":"https://github.com/alannawzadamin/dat-graph/blob/HEAD/dat_graph/graph_ops.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fb2341d14e8b8291"}},{"code_sha256_prefix":"3ad1dab50e710906","entry":"orient_v_structures","repo":"alannawzadamin/dat-graph","repo_kind":"official","path":"dat_graph/graph_ops.py","file_url":"https://github.com/alannawzadamin/dat-graph/blob/HEAD/dat_graph/graph_ops.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3ad1dab50e710906"}},{"code_sha256_prefix":"57d67d61c1fcda0f","entry":"remove_edges_to_dag","repo":"alannawzadamin/dat-graph","repo_kind":"official","path":"dat_graph/graph_ops.py","file_url":"https://github.com/alannawzadamin/dat-graph/blob/HEAD/dat_graph/graph_ops.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"57d67d61c1fcda0f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}