Single proxy control.
Single proxy control.
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单一代理控制。
DOI：
10.1093/biomtc/ujae027
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发表时间：
2024
期刊：
影响因子：
1.9
通讯作者：
TchetgenTchetgen,EricJ
中科院分区：
文献类型：
--
作者：
Park,Chan;Richardson,DavidB;TchetgenTchetgen,EricJ
在非实验研究中，有时会使用负控制变量来检测隐藏因素的混杂存在。阴性对照结果(NCO)是受未观察到的暴露对结果影响的混杂因素影响的结果，但不受暴露的因果影响。Tchetgen Tchetgen推出了控制结果校准方法(COCA)，作为一种正式的NCO反事实方法，用于检测和纠正残留的混杂偏差。为了识别，Coca将NCO视为容易出错的感兴趣的无需治疗的反事实结果的代理，并涉及将NCO回归到无需治疗的反事实结果，以及一个保持等级的结构模型，该结构模型假定了恒定的个人层面的因果效应。在这项工作中，我们为受试者的平均因果效应建立了非参数的Coca辨识，而不需要保序，因此适应了不同单元之间的不受限制的效应异质性。这一非参数识别结果具有重要的实际意义，因为它提供了单代理混淆控制，而不是最近提出的依赖于一对混淆代理进行识别的近端因果推断。对于COCA估计，我们提出了三种不同的策略：(I)扩展倾向计分方法，(Ii)结果桥函数方法，和(Iii)双稳健方法。最后，我们在一个评估寨卡病毒爆发对巴西出生率的因果影响的应用程序中说明了所提出的方法。
Negative control variables are sometimes used in nonexperimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model, which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single-proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose 3 separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.