摘要翻译：
在最近关于估计异质性治疗效果的文献中，每一种提出的方法都对干预效果和明确估计哪些亚群体做出了自己的一套限制性假设。此外，除了人工检查之外，大多数文献没有提供任何机制来确定哪些亚种群受影响最大，也很少保证确定的亚种群的正确性。因此，我们提出了治疗效果子集扫描(TESS)，这是一种新的方法，可以发现在随机实验中哪个亚群体受治疗的影响最大。我们将这一挑战描述为一个模式检测问题，在这个问题上，我们有效地最大化子种群上的非参数扫描统计量。此外，我们确定了由于干预而经历最大分布变化的亚种群，同时对干预的效果或潜在的数据生成过程做出最小的假设。除了该算法外，我们还证明了渐近I型和II型误差是可以控制的，并给出了检测一致性的充分条件--即精确识别受影响的子群体。最后，我们通过从一个著名的程序评估研究中发现模拟和现实世界数据中的异构处理效果来验证该方法的有效性。
---
英文标题：
《Efficient Discovery of Heterogeneous Treatment Effects in Randomized
  Experiments via Anomalous Pattern Detection》
---
作者：
Edward McFowland III, Sriram Somanchi, Daniel B. Neill
---
最新提交年份：
2018
---
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
--
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。
--
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
--

---
英文摘要：
  In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention's effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides no mechanism to identify which subpopulations are the most affected--beyond manual inspection--and provides little guarantee on the correctness of the identified subpopulations. Therefore, we propose Treatment Effect Subset Scan (TESS), a new method for discovering which subpopulation in a randomized experiment is most significantly affected by a treatment. We frame this challenge as a pattern detection problem where we efficiently maximize a nonparametric scan statistic over subpopulations. Furthermore, we identify the subpopulation which experiences the largest distributional change as a result of the intervention, while making minimal assumptions about the intervention's effects or the underlying data generating process. In addition to the algorithm, we demonstrate that the asymptotic Type I and II error can be controlled, and provide sufficient conditions for detection consistency--i.e., exact identification of the affected subpopulation. Finally, we validate the efficacy of the method by discovering heterogeneous treatment effects in simulations and in real-world data from a well-known program evaluation study.
---
PDF链接：
https://arxiv.org/pdf/1803.09159