因果重叠效应：一种累积固定效应方法
Causal Overlap Effects: A Cumulative Fixed Effect Approach
AI总结：
研究社会情境暴露时长对结果的重叠效应，提出多维处理因果框架，其重叠效应含时长和内容，还开发累积固定效应方法估计因果重叠效应，且该方法无偏。
摘要：
社会科学家常问增加接触社会情境的持续时间对个人结果的影响，即重叠效应。过去研究采用单维处理效应框架估计重叠效应，有重要限制。本文提出多维处理的新因果框架，重叠效应包括持续时间和重叠内容。该框架允许更灵活和情境丰富的效应异质性方法，难以用传统固定效应估计。因此开发新的累积固定效应（CFE）方法，可从三波个体面板数据估计一系列有趣的异质因果重叠效应。我们表明CFE方法即使在高度非线性模拟中也是无偏的，并讨论了假设和扩展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
