New assumptions for identifying joint expectation in causal inference

This paper introduces new assumptions, comonotonicity and countercomonotonicity, for identifying the joint expectation of functions of potential outcomes in causal inference. These assumptions provide a unified approach for discrete, continuous, and mixed outcome variables. When these assumptions are not met, the paper derives sharp bounds for specific classes of functions, offering new results for bounding moments of individual causal effects, which are crucial for understanding effect heterogeneity. The research also includes corresponding estimation methods and demonstrates their application through simulations and real-world data. AI

IMPACT Introduces novel statistical methods for analyzing potential outcomes, advancing causal inference research.

RANK_REASON Academic paper published on arXiv detailing new methodology in causal inference. [lever_c_demoted from research: ic=1 ai=1.0]

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