패널 데이터 인과 추론을 위한 자기회귀 모형 및 주 차원 오피오이드 정책 적용
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Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption
출처 기사
RAND Corporation (United States) | Apr 09, 2026
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AI 번역 · 원문 보기
While autoregressive models offer valuable insights, they should be supplemented with qualitative data to capture the human aspect of opioid policies.
AI 번역 · 원문 보기
Relying on complex models may overlook important contextual factors that influence the effectiveness of opioid policies in different states.
AI 번역 · 원문 보기
Analyzing panel data through autoregressive models is a crucial step towards developing more effective and targeted opioid interventions at the state level.
AI 번역 · 원문 보기
The use of autoregressive models can significantly enhance our understanding of the causal impacts of state-level opioid policies.
AI 번역 · 원문 보기
Staggered treatment adoption in opioid policies can lead to misleading conclusions if not properly accounted for in causal analyses.
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