摘要翻译：
对异质性治疗效果的灵活估计是许多统计学挑战的核心，如个性化医疗和优化资源分配。在本文中，我们发展了一个一般的两步算法，用于观察研究中的异构治疗效果估计。我们首先估计边际效应和治疗倾向，以便形成一个分离信号因果成分的目标函数。然后，我们对这个数据自适应目标函数进行优化。与现有方法相比，我们的方法有几个优点。从实际应用的角度来看，我们的方法灵活易用：在这两个步骤中，我们可以使用任何损失最小化方法，如惩罚回归、深度神经网络或boosting；而且，这些方法可以通过交叉验证进行微调。同时，在惩罚核回归的情况下，我们证明了我们的方法具有准oracle性质：即使对边际效应和治疗倾向的先导估计不是特别准确，我们也达到了与对这两个干扰成分有先验知识的oracle相同的误差范围。我们在各种模拟设置中实现了基于惩罚回归、核岭回归和boosting的方法的变体，并找到了相对于现有基线的有希望的性能。
---
英文标题：
《Quasi-Oracle Estimation of Heterogeneous Treatment Effects》
---
作者：
Xinkun Nie, Stefan Wager
---
最新提交年份：
2020
---
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
--
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：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

---
英文摘要：
  Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation setups, and find promising performance relative to existing baselines.
---
PDF链接：
https://arxiv.org/pdf/1712.04912