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Causal explanations just got scalable鈥擯robabilistic Causal Impact bridges the gap between rigorous causality and practical application, enabling nuanced insights from large datasets.
By transforming LLM outputs into probabilistic programs, this approach slashes the computational cost of generating multiple code samples without sacrificing quality.
