摘要翻译：
在本文中，我们提出了用于计量经济学的深度学习技术，特别是用于因果推断和估计个人以及平均治疗效果。本文的贡献有两个方面：1。为了估计个体和平均治疗效果的广义邻域匹配，我们分析了使用自动编码器进行降维，同时保持嵌入空间中数据点之间的局部邻域结构。这种基于深度学习的技术在估计治疗效果方面优于简单的k近邻匹配，尤其是当数据点具有多个特征/协变量但位于高维空间的低维流形中时。我们还观察到比流形学习方法更好的邻居匹配性能。2.倾向评分匹配是一种特殊和流行的匹配方法，用于估计平均和个体治疗效果。我们提出了使用深度神经网络(DNNs)来进行倾向评分匹配，并为此提出了一个称为PropensityNet的网络。这是传统上用于估计倾向得分的逻辑回归技术的推广，我们通过经验证明DNNs在倾向得分匹配方面比逻辑回归表现得更好。这两种方法的代码将很快在Github上提供:https://Github.com/vikas84bf
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英文标题：
《Deep Learning for Causal Inference》
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作者：
Vikas Ramachandra
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最新提交年份：
2018
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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：
  In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the use of autoencoders for dimensionality reduction while maintaining the local neighborhood structure among the data points in the embedding space. This deep learning based technique is shown to perform better than simple k nearest neighbor matching for estimating treatment effects, especially when the data points have several features/covariates but reside in a low dimensional manifold in high dimensional space. We also observe better performance than manifold learning methods for neighbor matching. 2. Propensity score matching is one specific and popular way to perform matching in order to estimate average and individual treatment effects. We propose the use of deep neural networks (DNNs) for propensity score matching, and present a network called PropensityNet for this. This is a generalization of the logistic regression technique traditionally used to estimate propensity scores and we show empirically that DNNs perform better than logistic regression at propensity score matching. Code for both methods will be made available shortly on Github at: https://github.com/vikas84bf
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PDF链接：
https://arxiv.org/pdf/1803.00149