必要的和可能足够的测试，以找到有效的仪器 变数

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摘要翻译：
可以从数据中找到工具变量吗？虽然工具变量(IV)方法被广泛用于确定因果效应，但从观察数据检验其有效性仍然是一个挑战。这是因为静脉注射的有效性取决于两个假设，排除和假设随机，这在很大程度上被认为是无法从数据中检验的。本文证明了在一定条件下，工具变量的检验是可能的。我们在先前关于必要检验的工作的基础上推导出一个检验，它表征了作为有效工具的几率，从而产生了“必要和可能充分”的名称。该检验通过定义无效-IV和有效-IV因果模型作为贝叶斯生成模型，并根据观测数据比较它们的边际似然来工作。当所有变量都是离散的时，我们还提供了一种有效地计算这些边际似然的方法。受过去工作中提出的IV测试的一个开放问题的启发，我们在一组针对二进制数据的广泛模拟中评估了该测试。我们发现，当仪器遵循单调性时，检验最有力----对治疗的作用不是递减就是递增----具有中等强度至弱强度；顺便说一句，这种仪器通常用于观察性研究。在假设随机和排除中，它以较高的能力检测排除违规。将两项关于工具变量的开创性研究和《美国经济评论》最近的五项研究的检验应用于IVs表明，许多工具可能是有缺陷的，至少当所有变量都离散化时是这样。提出的测试开启了数据驱动的验证和工具变量搜索的可能性。
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英文标题：
《Necessary and Probably Sufficient Test for Finding Valid Instrumental
  Variables》
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作者：
Amit Sharma
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最新提交年份：
2018
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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：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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英文摘要：
  Can instrumental variables be found from data? While instrumental variable (IV) methods are widely used to identify causal effect, testing their validity from observed data remains a challenge. This is because validity of an IV depends on two assumptions, exclusion and as-if-random, that are largely believed to be untestable from data. In this paper, we show that under certain conditions, testing for instrumental variables is possible. We build upon prior work on necessary tests to derive a test that characterizes the odds of being a valid instrument, thus yielding the name "necessary and probably sufficient". The test works by defining the class of invalid-IV and valid-IV causal models as Bayesian generative models and comparing their marginal likelihood based on observed data. When all variables are discrete, we also provide a method to efficiently compute these marginal likelihoods.   We evaluate the test on an extensive set of simulations for binary data, inspired by an open problem for IV testing proposed in past work. We find that the test is most powerful when an instrument follows monotonicity---effect on treatment is either non-decreasing or non-increasing---and has moderate-to-weak strength; incidentally, such instruments are commonly used in observational studies. Among as-if-random and exclusion, it detects exclusion violations with higher power. Applying the test to IVs from two seminal studies on instrumental variables and five recent studies from the American Economic Review shows that many of the instruments may be flawed, at least when all variables are discretized. The proposed test opens the possibility of data-driven validation and search for instrumental variables.
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PDF链接：
https://arxiv.org/pdf/1812.01412

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关键词：instrumental econometrics Multivariate Econometric Instruments test testing 变量 causal 检验