Sensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions
Sensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions
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DOI：
10.3102/1076998610383985
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发表时间：
2011-08-01
影响因子：
2.4
通讯作者：
Vinokur, Amiram D.
中科院分区：
文献类型：
--
作者：
Jo, Booil;Vinokur, Amiram D.
当因果效应的识别依赖于关于未识别参数的不可检验的假设时，因果效应估计的敏感性经常受到质疑。在这种情况下，为了正确解释因果效应估计值，推导因果参数的界限或探索估计值对科学上合理的替代假设的敏感性可能至关重要。在这篇文章中，作者提出了一种实用的方法的边界和敏感性分析，其中多个识别假设相结合，以构建更严格的共同边界。特别是，作者专注于使用竞争识别假设，对相同的非识别参数施加不同的限制。由于这些假设是通过相同的参数连接的，因此可以在它们之间进行直接转换。基于这种交叉可译性，数据中的各种信息，通过替代假设，可以有效地结合起来，构建因果效应的更严格的界限。灵活性的建议的方法被证明集中在估计的编译器平均因果效应（CACE）在一个随机的求职干预试验，遭受不遵守和随后的失踪的结果。
When identification of causal effects relies on untestable assumptions regarding nonidentified parameters, sensitivity of causal effect estimates is often questioned. For proper interpretation of causal effect estimates in this situation, deriving bounds on causal parameters or exploring the sensitivity of estimates to scientifically plausible alternative assumptions can be critical. In this article, the authors propose a practical way of bounding and sensitivity analysis, where multiple identifying assumptions are combined to construct tighter common bounds. In particular, the authors focus on the use of competing identifying assumptions that impose different restrictions on the same nonidentified parameter. Since these assumptions are connected through the same parameter, direct translation across them is possible. Based on this cross-translatability, various information in the data, carried by alternative assumptions, can be effectively combined to construct tighter bounds on causal effects. Flexibility of the suggested approach is demonstrated focusing on the estimation of the complier average causal effect (CACE) in a randomized job search intervention trial that suffers from noncompliance and subsequent missing outcomes.