One pervasive task found throughout the empirical sciences is todetermine the effect of interventions from observational(non-experimental) data. It is well-understood that assumptions arenecessary to perform causal inferences, which are commonly articulatedthrough causal diagrams (Pearl, 2000). Despite the power of thisapproach, there are settings where the knowledge necessary to fullyspecify a causal diagram may not be available, particularly incomplex, high-dimensional domains. In this talk, I will present tworecent causal effect identification results that relax the stringentrequirement of fully specifying a causal diagram. The first is a newgraphical modeling tool called cluster DAGs (for short, C-DAGs) thatallows for the specification of relationships among clusters ofvariables, while the relationships between the variables within acluster are left unspecified . The second includes a completecalculus and algorithm for effect identification from a PartialAncestral Graph (PAG), which represents a Markov equivalence class ofcausal diagrams, fully learnable from observational data . Theseapproaches are expected to help researchers and data scientists toidentify novel effects in real-world domains, where knowledge islargely unavailable and coarse.
 Anand, T. V., Ribeiro, A. H., Tian, J., & Bareinboim, E. (2023).Causal Effect Identification in Cluster DAGs. In Proceedings of theAAAI Conference on Artificial Intelligence, Vol. 37, No. 10, pp.12172-12179.
 Jaber, A., Ribeiro, A., Zhang, J., & Bareinboim, E. (2022). Causalidentification under markov equivalence: Calculus, algorithm, andcompleteness. Advances in Neural Information Processing Systems, 35,3679-3690.
Texto informado pelo autor.
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Apoiadores / Parceiros / Patrocinadores
Adele Helena Ribeiro
Dr. Adèle Helena Ribeiro is a postdoctoral researcher in the DataScience in Biomedicine Lab at the University of Marburg, Germany.Previously, she held a postdoctoral researcher position in the CausalAI Lab, at Columbia University, USA. Her research is centered aroundadvancing the capabilities of machine learning and artificialintelligence tools by incorporating causal and counterfactualreasoning. She is actively working on the development of causalinference and learning tools, with the goal of bridging the gapbetween causality theory and real-world applications, especially inthe realm of Health Sciences. She received her Ph.D., M.Sc., and B.Sc.degrees from the Institute of Mathematics and Statistics of theUniversity of Sao Paulo (USP), Brazil. For more information, you canvisit her academic webpage.