MSc Program in Data Science and Machine/Statistical Learning - Spring 2025 Lecture Series

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ΘΕΜΑ: MSc Program in Data Science and Machine/Statistical Learning - Spring 2025 Lecture Series
ΑΠΟΣΤΟΛΕΑΣ: Ειρήνη Βλαχάκη <vlachaki@xxxxxxxxxxxx> 




🎓 MSc Program in Data Science and Machine/Statistical Learning Spring 2025 Lecture Series
📅 Date: Tuesday, March 11, 2025 | ⏰ Time: 11:30 AM
📍 Location: Meeting Room Vassileios Dougalis, STEP C, KEEK Building, FORTH ________________________________________
Guest Lecture
🔹 Speaker: Liam Solus (KTH Royal Institute of Technology)
🔹 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.