摘要翻译：
本文研究并提出了几种归因建模方法，这些方法量化了如何将收入归因于在线广告投入。我们采用并进一步发展了相对重要性方法，该方法基于已被广泛研究和使用的回归模型，以调查广告努力与市场反应（收入）之间的关系。相对重要性法旨在分解和分配对回归模型决定系数(R^2)的边际贡献作为属性值。特别地，我们采用了两种可供选择的子方法来执行这种分解：优势分析和相对权重分析。此外，我们还证明了分解方法从标准线性模型到加性模型的扩展。我们声称，我们的新方法在建模潜在关系和计算属性值方面更加灵活和准确。我们用仿真例子来证明我们的新方法比传统方法有更好的性能。我们用一个真实的广告活动数据集进一步说明了我们提出的方法的价值。
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英文标题：
《Revenue-based Attribution Modeling for Online Advertising》
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作者：
Kaifeng Zhao, Seyed Hanif Mahboobi, Saeed Bagheri
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最新提交年份：
2017
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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：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 paper examines and proposes several attribution modeling methods that quantify how revenue should be attributed to online advertising inputs. We adopt and further develop relative importance method, which is based on regression models that have been extensively studied and utilized to investigate the relationship between advertising efforts and market reaction (revenue). Relative importance method aims at decomposing and allocating marginal contributions to the coefficient of determination (R^2) of regression models as attribution values. In particular, we adopt two alternative submethods to perform this decomposition: dominance analysis and relative weight analysis. Moreover, we demonstrate an extension of the decomposition methods from standard linear model to additive model. We claim that our new approaches are more flexible and accurate in modeling the underlying relationship and calculating the attribution values. We use simulation examples to demonstrate the superior performance of our new approaches over traditional methods. We further illustrate the value of our proposed approaches using a real advertising campaign dataset.
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PDF链接：
https://arxiv.org/pdf/1710.06561