实时竞价拍卖中广告的在线因果推理

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摘要翻译：
实时竞价(RTB)系统利用拍卖将用户印象程序化地分配给多个竞争的广告商，在数字广告中继续享有广泛的成功。评估此类广告的有效性仍然是研究和实践中一个挥之不去的挑战。本文提出了一个新的实验设计，对通过这种机制购买的广告进行因果推理。我们的方法利用了RTB系统中普遍存在的第一和第二价格拍卖的经济结构，嵌入在多臂强盗(MAB)设置中，用于在线自适应实验。我们通过一个改进的汤普森抽样(TS)算法来实现它，该算法估计广告的因果效应，同时通过学习最优投标策略来最大限度地减少广告客户的实验成本，从而使其从拍卖参与中获得的预期收益最大化。仿真结果表明，该方法不仅成功地实现了广告客户的目标，而且比传统的因果推断实验策略成本低得多。
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英文标题：
《Online Causal Inference for Advertising in Real-Time Bidding Auctions》
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作者：
Caio Waisman, Harikesh S. Nair, Carlos Carrion, Nan Xu
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最新提交年份：
2021
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分类信息：

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：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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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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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：
  Real-time bidding (RTB) systems, which leverage auctions to programmatically allocate user impressions to multiple competing advertisers, continue to enjoy widespread success in digital advertising. Assessing the effectiveness of such advertising remains a lingering challenge in research and practice. This paper presents a new experimental design to perform causal inference on advertising bought through such mechanisms. Our method leverages the economic structure of first- and second-price auctions, which are ubiquitous in RTB systems, embedded within a multi-armed bandit (MAB) setup for online adaptive experimentation. We implement it via a modified Thompson sampling (TS) algorithm that estimates causal effects of advertising while minimizing the costs of experimentation to the advertiser by simultaneously learning the optimal bidding policy that maximizes her expected payoffs from auction participation. Simulations show that not only the proposed method successfully accomplishes the advertiser's goals, but also does so at a much lower cost than more conventional experimentation policies aimed at performing causal inference.
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PDF链接：
https://arxiv.org/pdf/1908.08600

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关键词：econometrics Applications Environments Experimental Successfully advertising 实验 systems 印象 method