Universal Difference-in-Differences for Causal Inference in Epidemiology
流行病学因果推断中的通用双重差分AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以替代平行趋势的假设识别因果效应，并明确包含分位数处理效应，因此对应因果推断及分位数与分布效应。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
流行病学因果推断中的通用双重差分
- 研究问题
- 当二元、计数或多类别结果以及非加性混杂使平行趋势难以成立时，能否用其他限制识别包括分位数效应在内的因果效应？
- 方法与思路
- 作者以优势比等混杂假设替代平行趋势，结合暴露前结果与暴露的广义线性模型，提出参数及更稳健的半参数 UDiD 估计量。
- 材料陈述的贡献
- 摘要称新假设可支持识别多种效应，包括分位数处理效应，并以巴西寨卡疫情对出生率影响的应用展示估计方法。
- 阅读关注点
- 应核对优势比等混杂的精确定义、所需广义线性模型设定及效应识别推导，并比较参数与半参数估计各自的稳健性条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出新假设和稳健性的完整条件，通用名称不意味着任意混杂下均能识别。
原文摘要
Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and post-exposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms, and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, including if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits non-additive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no-treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called Universal Difference-in-differences (UDiD). Both fully parametric and more robust semiparametric UDiD estimators are described and illustrated in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil.