论文标题
感官和灵敏度分析：由于未观察到的混杂而引起的简单事后分析
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
论文作者
论文摘要
普遍承认的是，没有已知机制的观察到的关联必须存在因果估计。但是，观察数据中的因果估计通常取决于“没有未观察到的混杂”的（无法测试的）假设。违反这种假设可能会引起偏见实际上估计。原则上，这种偏见可能无效或扭转研究的结论。但是，在某些情况下，我们可能希望相对于“大型”估计效果，未观察到的混杂因素的影响是弱的，因此定性结论对未观察到的混杂因素而言是强大的。本文的目的是开发\ emph {austen绘图}，这是一种灵敏度分析工具，可以通过使未经观察的混杂引起的潜在偏见更加轻松地提出来帮助此类判断。我们从混杂因素对治疗作业和结果的影响程度上进行了正式的混淆。对于目标偏差的目标水平，奥斯丁图显示了诱导偏见水平所需的最低治疗值和结果影响。然后，领域专家可以对这种强大的混杂因素是否合理做出主观判断。为了帮助这一判断，奥斯丁图还显示了（组）观察到的协变量的估计影响强度。奥斯丁绘制了inibens的经典灵敏度分析方法[IMB03]。至关重要的是，奥斯丁的图允许对观察到的数据进行建模并产生初始估计值的任何方法。我们使用多种机器学习方法来评估几个真正的因果推理问题的偏见来说明该工具。代码可从https://github.com/anishazaveri/austen_plots获得
It is a truth universally acknowledged that an observed association without known mechanism must be in want of a causal estimate. However, causal estimation from observational data often relies on the (untestable) assumption of `no unobserved confounding'. Violations of this assumption can induce bias in effect estimates. In principle, such bias could invalidate or reverse the conclusions of a study. However, in some cases, we might hope that the influence of unobserved confounders is weak relative to a `large' estimated effect, so the qualitative conclusions are robust to bias from unobserved confounding. The purpose of this paper is to develop \emph{Austen plots}, a sensitivity analysis tool to aid such judgments by making it easier to reason about potential bias induced by unobserved confounding. We formalize confounding strength in terms of how strongly the confounder influences treatment assignment and outcome. For a target level of bias, an Austen plot shows the minimum values of treatment and outcome influence required to induce that level of bias. Domain experts can then make subjective judgments about whether such strong confounders are plausible. To aid this judgment, the Austen plot additionally displays the estimated influence strength of (groups of) the observed covariates. Austen plots generalize the classic sensitivity analysis approach of Imbens [Imb03]. Critically, Austen plots allow any approach for modeling the observed data and producing the initial estimate. We illustrate the tool by assessing biases for several real causal inference problems, using a variety of machine learning approaches for the initial data analysis. Code is available at https://github.com/anishazaveri/austen_plots
