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New method bounds causal effect moments using marginal data

Researchers have developed a new method for identifying and bounding the central moments of individual causal effects (ICE) by utilizing only the marginal central moments of potential outcomes. This approach is more practical for empirical applications than existing methods that require knowledge of the full marginal distributions of potential outcomes. The paper demonstrates the utility of this method through two case studies. AI

RANK_REASON The item is an academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

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New method bounds causal effect moments using marginal data

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Naoya Hashimoto, Yuta Kawakami, Jin Tian ·

    Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information

    arXiv:2607.04957v1 Announce Type: cross Abstract: Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (IC…

  2. arXiv stat.ML TIER_1 English(EN) · Jin Tian ·

    Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information

    Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (ICE) characterize the shape of the ICE distribution,…