摘要翻译：
我们考虑在随着时间的推移分配多个治疗并且治疗可能对未来的结果或被治疗单位的状态产生因果影响的情况下对治疗效果的估计。我们提出了一个双/去偏机器学习框架的扩展来估计治疗的动态效果，它可以被看作是动态治疗机制中$G$-估计的Neyman正交（局部鲁棒）交叉拟合版本。我们的方法适用于一类被称为结构嵌套均值模型的非线性动态处理模型，并允许使用机器学习方法来控制潜在的高维状态变量，在保证均方误差的前提下，同时允许参数估计和构造感兴趣的结构参数的置信区间。这些结构参数可用于以参数率对任何目标动态策略进行非策略评估，但数据生成过程受到半参数限制。我们的工作基于$G$-估计中典型的递归剥离过程，并在每个阶段制定了一个强凸目标，这允许我们在多个方向上扩展$G$-估计框架：i）提供有限样本保证，ii)在任意函数空间中估计关于固定单元特征的非线性效应异质性，使异质性效应的RLearner算法能够动态模拟，iii)允许目标结构函数的高维稀疏参数化，使通过递归lasso算法自动选择模型。我们还提供数据的保证，从一个单一的处理单位在长期和平稳条件下。
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英文标题：
《Double/Debiased Machine Learning for Dynamic Treatment Effects via
  g-Estimation》
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作者：
Greg Lewis, Vasilis Syrgkanis
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最新提交年份：
2021
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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：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：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：
  We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes or the state of the treated unit. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments, which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of $g$-estimation in the dynamic treatment regime. Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of machine learning methods to control for potentially high dimensional state variables, subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the structural parameters of interest. These structural parameters can be used for off-policy evaluation of any target dynamic policy at parametric rates, subject to semi-parametric restrictions on the data generating process. Our work is based on a recursive peeling process, typical in $g$-estimation, and formulates a strongly convex objective at each stage, which allows us to extend the $g$-estimation framework in multiple directions: i) to provide finite sample guarantees, ii) to estimate non-linear effect heterogeneity with respect to fixed unit characteristics, within arbitrary function spaces, enabling a dynamic analogue of the RLearner algorithm for heterogeneous effects, iii) to allow for high-dimensional sparse parameterizations of the target structural functions, enabling automated model selection via a recursive lasso algorithm. We also provide guarantees for data stemming from a single treated unit over a long horizon and under stationarity conditions.
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PDF链接：
https://arxiv.org/pdf/2002.07285