Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
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混杂性结局依赖抽样下的双重稳健近端因果推断
- 研究问题
- 当潜在因素同时影响处理、结局和样本选择时，能否利用负对照代理变量估计因果优势比，并减轻桥函数误设的影响？
- 方法与思路
- 作者在处理效应优势比模型下同时使用处理混杂桥与结局混杂桥，构造针对这两个干扰函数的双重稳健估计量。
- 材料陈述的贡献
- 摘要称在所需条件下，任一桥函数模型正确即可获得一致且渐近正态的估计；模拟展示混杂与选择偏差调整及区间校准。
- 阅读关注点
- 应核对负对照的有效性、桥函数存在与识别条件、双重稳健结论的其他要求，以及渐近正态性和方差估计的条件。
- 阅读边界
- 这是预印本，不能据此确认同行评审或期刊接收；摘要未完整列出识别和正则条件，双重稳健不意味着无需有效代理变量。
原文摘要
Unmeasured confounding and selection bias are often of concern in observational studies and may invalidate a causal analysis if not appropriately accounted for. Under outcome-dependent sampling, a latent factor that has causal effects on the treatment, outcome, and sample selection process may cause both unmeasured confounding and selection bias, rendering standard causal parameters unidentifiable without additional assumptions. Under an odds ratio model for the treatment effect, Li et al. 2022 established both proximal identification and estimation of causal effects by leveraging a pair of negative control variables as proxies of latent factors at the source of both confounding and selection bias. However, their approach relies exclusively on the existence and correct specification of a so-called treatment confounding bridge function, a model that restricts the treatment assignment mechanism. In this article, we propose doubly robust estimation under the odds ratio model with respect to two nuisance functions -- a treatment confounding bridge function and an outcome confounding bridge function that restricts the outcome law, such that our estimator is consistent and asymptotically normal if either bridge function model is correctly specified, without knowing which one is. Thus, our proposed doubly robust estimator is potentially more robust than that of Li et al. 2022. Our simulations confirm that the proposed proximal estimators of an odds ratio causal effect can adequately account for both residual confounding and selection bias under stated conditions with well-calibrated confidence intervals in a wide range of scenarios, where standard methods generally fail to be consistent. In addition, the proposed doubly robust estimator is consistent if at least one confounding bridge function is correctly specified.