Frank Kenneth A, Lin Qinyun, Xu Ran, Maroulis Spiro, Mueller Anna
Michigan State University, USA.
University of Chicago, USA.
Soc Sci Res. 2023 Feb;110:102815. doi: 10.1016/j.ssresearch.2022.102815. Epub 2022 Nov 17.
Social scientists seeking to inform policy or public action must carefully consider how to identify effects and express inferences because actions based on invalid inferences may not yield the intended results. Recognizing the complexities and uncertainties of social science, we seek to inform inevitable debates about causal inferences by quantifying the conditions necessary to change an inference. Specifically, we review existing sensitivity analyses within the omitted variables and potential outcomes frameworks. We then present the Impact Threshold for a Confounding Variable (ITCV) based on omitted variables in the linear model and the Robustness of Inference to Replacement (RIR) based on the potential outcomes framework. We extend each approach to include benchmarks and to fully account for sampling variability represented by standard errors as well as bias. We exhort social scientists wishing to inform policy and practice to quantify the robustness of their inferences after utilizing the best available data and methods to draw an initial causal inference.
寻求为政策或公共行动提供信息的社会科学家必须仔细考虑如何识别影响并表达推论，因为基于无效推论的行动可能无法产生预期结果。认识到社会科学的复杂性和不确定性，我们试图通过量化改变推论所需的条件，为关于因果推论的必然辩论提供信息。具体而言，我们回顾了遗漏变量和潜在结果框架内现有的敏感性分析。然后，我们基于线性模型中的遗漏变量提出了混杂变量的影响阈值（ITCV），并基于潜在结果框架提出了推论对替代的稳健性（RIR）。我们扩展了每种方法，以纳入基准，并充分考虑标准误差和偏差所代表的抽样变异性。我们敦促希望为政策和实践提供信息的社会科学家，在利用现有最佳数据和方法得出初步因果推论后，对其推论的稳健性进行量化。
