论文标题
具有无效仪器变量的非线性结果模型的因果推断
Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables
论文作者
论文摘要
仪器变量方法被广泛用于在存在未衡量的混杂因素的情况下推断因果效应。非线性结果模型的现有仪器变量方法需要严格的可识别性条件。本文考虑了灵活的半参数势结果模型，该模型允许使用无效的仪器。我们提出了新的可识别性条件，以识别大多数仪器变量有效时。我们为新的平均结构功能和条件平均治疗效果设计了一种新颖的推理程序。我们建立了提议的估计量的渐近正态性，并通过引导构建因果估计的构建置信区间。在大规模的模拟研究中证明了所提出的方法，并应用于推断收入对房屋所有权的影响。
Instrumental variable methods are widely used for inferring the causal effect in the presence of unmeasured confounders. Existing instrumental variable methods for nonlinear outcome models require stringent identifiability conditions. This paper considers a flexible semi-parametric potential outcome model that allows for possibly invalid instruments. We propose new identifiability conditions to identify the causal parameters when the majority of the instrumental variables are valid. We devise a novel inference procedure for a new average structural function and the conditional average treatment effect. We establish the asymptotic normality of the proposed estimators and construct confidence intervals for the causal estimands by bootstrap. The proposed method is demonstrated in large-scale simulation studies and is applied to infer the effect of income on house ownership.
