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Robust learning methods can be systematically compared under distributional shifts, revealing unexpected complementary behaviors that enhance performance with partial information.
CURL effectively harnesses LLMs to stabilize CATE estimation, leading to improved performance in personalized interventions across multiple benchmarks.
