{"id":138585,"date":"2022-04-25T02:05:16","date_gmt":"2022-04-25T07:05:16","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2022\/04\/deepmind-mila-google-brain-enable-generalization-capabilities-for-causal-graph-structure-induction"},"modified":"2022-04-25T02:05:16","modified_gmt":"2022-04-25T07:05:16","slug":"deepmind-mila-google-brain-enable-generalization-capabilities-for-causal-graph-structure-induction","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2022\/04\/deepmind-mila-google-brain-enable-generalization-capabilities-for-causal-graph-structure-induction","title":{"rendered":"DeepMind, Mila &amp; Google Brain Enable Generalization Capabilities for Causal Graph Structure Induction"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/deepmind-mila-google-brain-enable-generalization-capabilities-for-causal-graph-structure-induction2.jpg\"><\/a><\/p>\n<p>Discovering a system\u2019s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.<\/p>\n<p>A research team from DeepMind, Mila \u2013 University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper <em>Learning to Induce Causal Structure<\/em>, proposing a neural network architecture that learns the graph structure of observational and\/or interventional data via supervised training on synthetic graphs. The team\u2019s proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.<\/p>\n<p>The team summarizes their main contributions as:<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discovering a system\u2019s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy [\u2026]<\/p>\n","protected":false},"author":359,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,39,6],"tags":[],"class_list":["post-138585","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-economics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/138585","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/359"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=138585"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/138585\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=138585"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=138585"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=138585"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}