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Explore causal reasoning as a transformative approach to data science that shifts focus from "what happened" to "what would happen if" in this 46-minute conference talk. Learn how to elevate predictive outcomes through causal inference while understanding when and how to apply these powerful techniques in real-world contexts. Discover the value of "causal thinking" as a mental exercise that enhances project rigor, even when full causal inference implementation may not be feasible. Master the fundamentals of Directed Acyclic Graphs (DAGs) as visual tools for mapping data generation processes and understanding system relationships between parameters and their dependencies. Examine the concept of identifiability and how to determine minimum parameter sets required for answering cause-and-effect questions. Gain practical insights through real-world use cases that demonstrate how causal graph construction and critical assumption assessment can lead to deeper understanding of data possibilities. Develop a nuanced appreciation for both the potential and limitations of causal inference techniques, equipping yourself to assess when causality is the appropriate tool for specific data science challenges and how causal thinking can enhance analytical work with real-world datasets.
