\ud83c\udf93 MSc Program in Data Science and Machine\/Statistical Learning
Spring 2025 Lecture Series
\ud83d\udcc5 Date: Tuesday, March 11, 2025 | \u23f0 Time: 11:30 AM
\ud83d\udccd Location: Meeting Room Vassileios Dougalis, STEP C, KEEK Building, FORTH<\/p>\n\n\n\n
Guest Lecture
\ud83d\udd39 Speaker: Liam Solus (KTH Royal Institute of Technology)
\ud83d\udd39 Title: Causal Structure Identifiability via Submodel Geometry
Abstract
When modeling causal systems with directed graphs, methods for recovering the causal graph face a natural issue: Without any additional modeling assumptions, the graph is generally unidentifiable from only observational data. Consequently, costly experiments are often needed to identify the causal system and build causally-informed predictive models. However, structural identifiability typically improves when additional constraints are learned, such as model parameter homogeneities or context-specific invariances. One can then search a space of submodels defined by a choice of these additional constraints, returning more exact estimates of the causal graph without the need for experimental data. We will exhibit these methods via a pair of causal discovery algorithms in two cases; namely, large-scale categorical data and linear Gaussian models. Both of these model types are commonplace in industry, while also being cases where structural identifiability remains a theoretical challenge. In juxtaposition to previous results, structural identifiability for these models, as well as computational efficiency, are closely tied to the combinatorial and algebraic geometry of the submodels of interest.<\/p>\n","protected":false},"excerpt":{"rendered":"
\ud83c\udf93 MSc Program in Data Science and Machine\/Statistical LearningSpring 2025 Lecture Series\ud83d\udcc5 Date: Tuesday, March 11, 2025 | \u23f0 Time: 11:30 AM\ud83d\udccd Location: Meeting Room Vassileios Dougalis, STEP C, KEEK Building, FORTH Guest Lecture\ud83d\udd39 Speaker: Liam Solus (KTH Royal Institute of Technology)\ud83d\udd39 Title: Causal Structure Identifiability via Submodel GeometryAbstractWhen modeling causal systems with directed graphs, methods for recovering the causal graph face a natural issue: Without any additional modeling assumptions, the graph is generally unidentifiable from only observational data. Consequently, costly experiments are often needed to identify the causal system and build causally-informed predictive models. However, structural identifiability typically improves when additional constraints are learned, such as model parameter homogeneities or context-specific invariances. One can then search a space of submodels defined by a choice of these additional constraints, returning more exact estimates of the causal graph without the need for experimental data. We will exhibit these methods via a pair of causal discovery algorithms in two cases; namely, large-scale categorical data and linear Gaussian models. Both of these model types are commonplace in industry, while also being cases where structural identifiability remains a theoretical challenge. In juxtaposition to previous results, structural identifiability for these models, as well as computational efficiency, are closely tied to the combinatorial and algebraic geometry of the submodels of interest.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[139],"tags":[],"class_list":["post-798","post","type-post","status-publish","format-standard","hentry","category-announcement","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/posts\/798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/comments?post=798"}],"version-history":[{"count":1,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/posts\/798\/revisions"}],"predecessor-version":[{"id":3799,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/posts\/798\/revisions\/3799"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/categories?post=798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/tags?post=798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}