英文标题：
《Kernel Methods for Unobserved Confounding: Negative Controls, Proxies,
  and Instruments》
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作者：
Rahul Singh
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最新提交年份：
2021
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英文摘要：
  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.
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中文摘要：
消极控制是一种在存在未测量的混杂因素时学习治疗和结果之间因果关系的策略。然而，如果有两个辅助变量可用，则可以确定治疗效果：阴性对照治疗（对实际结果没有影响）和阴性对照治疗（不受实际治疗影响）。这些辅助变量也可以被视为一组传统控制变量的代理，它们与工具变量相似。我提出了一系列基于核岭回归的算法，用于学习负控制的非参数治疗效果。例子包括剂量反应曲线、分布偏移的剂量反应曲线和异质治疗效应。数据可以是离散的或连续的，也可以是低维、高维或无限维的。证明了一致相合性，并给出了有限样本收敛速度。我使用宾夕法尼亚州1989年至1991年间的一组独生子女数据，估计了吸烟对婴儿出生体重的剂量反应曲线，并对家庭收入引起的未观察到的混淆进行了调整。
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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