Causal Inference Tools
Lucy discusses the importance of defining assumptions in causal inference, particularly in observational settings. She highlights the use of propensity scores to create a counterfactual framework, allowing for comparisons between treatment groups based on their baseline characteristics. This method helps to adjust for confounding variables, making it possible to draw more reliable causal conclusions despite the absence of randomization.In this clip
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Practical AI
A casual conversation concerning causal inference
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