摘要翻译：
在这篇论文中，我们为应用研究人员提供了工具，这些工具重新利用计算机科学领域的机器学习中的现成方法，为随机对照试验（RCTs）的数据创建一个“发现引擎”。我们寻求解决的应用问题是，经济学家投入大量资源进行RCTs，包括收集一套丰富的候选结果度量。但考虑到对存在多重测试的推断的担忧，经济学家通常最终只探索可用数据可以用来测试的假设的一小部分。这使我们无法从每个RCT中提取尽可能多的信息，这反过来又损害了我们发展新理论或加强政策干预设计的能力。我们提出的解决方案结合了反向回归的基本直觉，其中感兴趣的因变量现在变成治疗分配本身，以及机器学习的方法，这些方法使用数据本身来灵活地识别结果是否有任何预测（或关于）治疗组状态的信号。这将导致具有适当的$P$-值的正确大小的测试，这些测试还有一个重要的优点，即易于在实践中实现。我们工作中仍然存在的一个开放挑战是如何有意义地解释这些方法发现的信号。
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英文标题：
《Machine-Learning Tests for Effects on Multiple Outcomes》
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作者：
Jens Ludwig, Sendhil Mullainathan, Jann Spiess
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最新提交年份：
2019
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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：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：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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英文摘要：
  In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs). The applied problem we seek to solve is that economists invest vast resources into carrying out RCTs, including the collection of a rich set of candidate outcome measures. But given concerns about inference in the presence of multiple testing, economists usually wind up exploring just a small subset of the hypotheses that the available data could be used to test. This prevents us from extracting as much information as possible from each RCT, which in turn impairs our ability to develop new theories or strengthen the design of policy interventions. Our proposed solution combines the basic intuition of reverse regression, where the dependent variable of interest now becomes treatment assignment itself, with methods from machine learning that use the data themselves to flexibly identify whether there is any function of the outcomes that predicts (or has signal about) treatment group status. This leads to correctly-sized tests with appropriate $p$-values, which also have the important virtue of being easy to implement in practice. One open challenge that remains with our work is how to meaningfully interpret the signal that these methods find.
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PDF链接：
https://arxiv.org/pdf/1707.01473