论文标题
未观察混杂的内核方法：负面对照，代理和仪器
Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments
论文作者
论文摘要
负面对照是在存在未衡量混杂的情况下学习治疗与结果之间因果关系的策略。但是，如果有两个辅助变量可用：阴性对照治疗（对实际结果没有影响），并且可以确定治疗效果，并且可以识别出负面对照的结果（不受实际治疗的影响）。这些辅助变量也可以视为传统控制变量集的代理，并且与仪器变量相似。我提出了一种基于内核脊回归的算法系列，用于学习非参数治疗效果，并具有阴性对照。例子包括剂量反应曲线，具有分布变化的剂量反应曲线以及异质治疗效果。数据可能是离散的或连续的，低，高或无限的维度。我证明一致性均匀，并提供有限的收敛速率。我使用宾夕法尼亚州1989年至1991年之间宾夕法尼亚州的单身人士出生的数据集，估算婴儿出生体重的剂量反应曲线，以调整婴儿的出生体重，以调整未观察到的混杂因素。
Negative control is a strategy for learning the causal relationship between treatment and outcome in the presence of unmeasured confounding. The treatment effect can nonetheless be identified if two auxiliary variables are available: a negative control treatment (which has no effect on the actual outcome), and a negative control outcome (which is not affected by the actual treatment). These auxiliary variables can also be viewed as proxies for a traditional set of control variables, and they bear resemblance to instrumental variables. I propose a family of algorithms based on kernel ridge regression for learning nonparametric treatment effects with negative controls. Examples include dose response curves, dose response curves with distribution shift, and heterogeneous treatment effects. Data may be discrete or continuous, and low, high, or infinite dimensional. I prove uniform consistency and provide finite sample rates of convergence. I estimate the dose response curve of cigarette smoking on infant birth weight adjusting for unobserved confounding due to household income, using a data set of singleton births in the state of Pennsylvania between 1989 and 1991.
