摘要翻译：
工具变量方法提供了一种在未观察到的混杂情况下估计因果效应的强有力的方法。但是，在应用它们时，一个关键的挑战是依赖于不可检验的“排除”假设，这些假设排除了工具变量和非治疗介导的反应之间的任何关系。在本文中，我们展示了如何执行一致的IV估计，尽管违反了排除假设。特别地，我们表明，当一个人有多个候选工具时，只有这些候选工具中的大多数--或者更一般地说，模态候选--反应关系--才需要有效来估计因果效应。我们的方法使用来自工具变量估计器集合的模态预测的估计。该技术应用简单，是一种“黑箱”技术，只要对每种有效仪器独立识别治疗效果，它就可以与任何仪器变量估计器一起使用。因此，它与最近基于机器学习的估计器兼容，这些估计器允许对复杂的、高维数据进行条件平均治疗效果(CATE)的估计。在实验上，我们使用基于深度网络的估计器的集合来实现条件平均治疗效果的精确估计，包括在一个具有挑战性的模拟孟德尔随机化问题上。
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英文标题：
《Valid Causal Inference with (Some) Invalid Instruments》
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作者：
Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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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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英文摘要：
  Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable "exclusion" assumptions that rule out any relationship between the instrument variable and the response that is not mediated by the treatment. In this paper, we show how to perform consistent IV estimation despite violations of the exclusion assumption. In particular, we show that when one has multiple candidate instruments, only a majority of these candidates---or, more generally, the modal candidate-response relationship---needs to be valid to estimate the causal effect. Our approach uses an estimate of the modal prediction from an ensemble of instrumental variable estimators. The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently. As such, it is compatible with recent machine-learning based estimators that allow for the estimation of conditional average treatment effects (CATE) on complex, high dimensional data. Experimentally, we achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators, including on a challenging simulated Mendelian Randomization problem.
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PDF链接：
https://arxiv.org/pdf/2006.11386