Explore causal effect identification from multiple incomplete data sources in this 36-minute lecture by Dr Santtu Tikka from the University of Jyväskylä, Finland. Delve into the challenges of determining interventional probability distributions without parametric assumptions. Learn about a novel search algorithm utilizing do-calculus rules to address advanced data-generating mechanisms and various observational and experimental source distributions. Discover how this approach extends to causal inference under context-specific independence relations. Examine topics such as the data-fusion problem, identifiability issues in causal inference, missing data challenges, and context-specific independence. Gain insights into labeled directed acyclic graphs, CSI-separation, and the complexity of decision problems in causal effect identification. Explore practical examples, including case-control design and derivations, to solidify understanding of these complex concepts.