摘要翻译：
本章涵盖政策评价的不同方法，以评估治疗或干预对感兴趣的结果的因果影响。作为因果推论的介绍，讨论从一个随机治疗的实验评估开始。然后，它回顾了基于可观察性选择（假设给定观察到的协变量的准随机治疗）、工具变量（诱导治疗中的准随机转移）、差异中的差异和变化中的变化（利用结果随时间的变化）以及回归不连续性和扭结（在运行变量的某个阈值上使用治疗分配的变化）的评估方法。本章讨论了特别适合于对治疗效果进行灵活（即半参数或非参数）建模的许多观察数据的方法，和/或通过应用机器学习以数据驱动的方式选择和控制协变量来选择和控制许多（即高维）观察到的协变量。这不仅有助于通过控制例如共同影响治疗和结果的因素来解决混杂问题，而且有助于根据可观察的协变量定义的子组之间的学习效果异质性，并优化地针对治疗最有效的组。
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英文标题：
《An introduction to flexible methods for policy evaluation》
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作者：
Martin Huber
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最新提交年份：
2019
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分类信息：

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：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：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：
  This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed covariates), instrumental variables (inducing a quasi-random shift in the treatment), difference-in-differences and changes-in-changes (exploiting changes in outcomes over time), as well as regression discontinuities and kinks (using changes in the treatment assignment at some threshold of a running variable). The chapter discusses methods particularly suited for data with many observations for a flexible (i.e. semi- or nonparametric) modeling of treatment effects, and/or many (i.e. high dimensional) observed covariates by applying machine learning to select and control for covariates in a data-driven way. This is not only useful for tackling confounding by controlling for instance for factors jointly affecting the treatment and the outcome, but also for learning effect heterogeneities across subgroups defined upon observable covariates and optimally targeting those groups for which the treatment is most effective.
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PDF链接：
https://arxiv.org/pdf/1910.00641