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We propose bounds on causal effects for missing outcomes, accommodating the scenario where missingness is an unobserved mixture of informative and non-informative components
출처 기사
RAND Corporation (United States) | Apr 01, 2026
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AI 번역 · 원문 보기
Relying on bounds for causal effects may oversimplify complex data issues, risking the integrity of conclusions drawn from such analyses.
AI 번역 · 원문 보기
Incorporating both informative and non-informative missingness into causal analysis is essential for developing robust public health strategies.
AI 번역 · 원문 보기
Bounding causal effects with missing outcomes enhances our understanding of data reliability, ensuring more accurate decision-making in research.
AI 번역 · 원문 보기
While bounding methods are innovative, their practical applicability in real-world scenarios remains uncertain and requires further exploration.
AI 번역 · 원문 보기
The complexity of bounding causal effects with unobserved missingness can lead to overgeneralizations, potentially misleading policymakers.
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