Causal Overlap Effects: A Cumulative Fixed Effect Approach
因果重叠效应：累积固定效应方法AI 解读 ↓
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因果重叠效应：累积固定效应方法
- 研究问题
- 如何同时刻画社会情境暴露的持续时间与内容，并估计包含未观测情境特征影响的异质性因果重叠效应？
- 方法与思路
- 作者提出多维处理框架，将重叠内容与持续时间共同纳入效应定义，并用三期个体面板构建累积固定效应方法。
- 材料陈述的贡献
- 摘要称该方法可估计多种异质性重叠效应，并在高度非线性模拟中表现为无偏，同时讨论相关假设与扩展。
- 阅读关注点
- 应核对多维处理下的效应定义、三期面板提供的识别信息，以及未观测特征兼具混杂与效应组成部分时的处理。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未列明识别假设，模拟无偏也不能直接外推。
原文摘要
Social scientists often ask about the effect of increasing one's duration of exposure to a social context on one's outcomes, i.e. the overlap effect. Past studies adopted a unidimensional treatment effect framework to estimate the effect of overlap, imposing important restrictions. In this paper, we propose a new causal framework of multidimensional treatments where the overlap effects include both the duration and the content of overlap, under which, for instance, the grandparent overlap effect is defined as the union of all causal effects of a grandparent's observed and unobserved characteristics (i.e., the content) on the grandchild across their shared life course (i.e., the duration). The multidimensional framework allows for a more flexible and context rich approach to effect heterogeneity, where unobserved contextual characteristics play two roles as unobserved confounders and as integral components of overlap effects -- overlap effects in this framework are not easily estimated with conventional fixed effects estimation. Hence, we develop a new cumulative fixed effects (CFE) approach that can estimate a range of interesting heterogeneous causal overlap effects from three-wave individual panel data. We show that the CFE approach is unbiased even in highly non-linear simulations, and we discuss assumptions and extensions.