摘要翻译：
泛函数逼近器，如人工神经网络，在给定足够的训练数据的情况下，可以任意学习大量的目标函数。这种灵活性是以执行参数推断的能力为代价的。我们通过提出一个基于Shapley-Taylor模型分解的通用框架来解决这个差距。在模型的Shapley值展开所跨越的空间中进行了代理参数回归分析。这允许测试感兴趣的标准假设。同时，该方法从非参数估计量的相合性和偏置性出发，对统计学习过程本身提供了新的见解。我们将该框架应用于模拟和真实世界随机实验中异构治疗效果的估计。我们引入了一个基于高阶Shapley-Taylor指数的显式处理函数。这可以用来识别潜在的复杂治疗通道，并有助于从实验环境中总结发现。更一般地说，所提出的方法允许对来自机器学习模型的结果进行标准化的使用和通信。
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英文标题：
《Parametric inference with universal function approximators》
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作者：
Andreas Joseph
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最新提交年份：
2020
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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
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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：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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英文摘要：
  Universal function approximators, such as artificial neural networks, can learn a large variety of target functions arbitrarily well given sufficient training data. This flexibility comes at the cost of the ability to perform parametric inference. We address this gap by proposing a generic framework based on the Shapley-Taylor decomposition of a model. A surrogate parametric regression analysis is performed in the space spanned by the Shapley value expansion of a model. This allows for the testing of standard hypotheses of interest. At the same time, the proposed approach provides novel insights into statistical learning processes themselves derived from the consistency and bias properties of the nonparametric estimators. We apply the framework to the estimation of heterogeneous treatment effects in simulated and real-world randomised experiments. We introduce an explicit treatment function based on higher-order Shapley-Taylor indices. This can be used to identify potentially complex treatment channels and help the generalisation of findings from experimental settings. More generally, the presented approach allows for a standardised use and communication of results from machine learning models.
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PDF链接：
https://arxiv.org/pdf/1903.04209