治疗分配方法的比较与应用 播放列表生成

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摘要翻译：
本研究对个体治疗分配的方法进行了系统的比较，个体治疗分配是一个在许多应用中出现的普遍问题，受到了经济学家、计算机科学家和社会科学家的广泛关注。我们将文献中提出的各种方法描述为三种通用方法：预测结果的学习模型、预测因果效应的学习模型和预测最佳治疗分配的学习模型。我们分析表明，结果或因果效应预测的优化与治疗分配的优化是不同的，因此我们应该更喜欢为治疗分配优化的学习模型。然后，在为每个用户选择播放列表生成的最佳算法的上下文中，我们对这三种方法进行了实证比较，以优化参与度。这是第一次在实际应用中大规模比较不同的治疗分配方法（基于超过5亿个单独的治疗分配）。我们的结果表明(i)与为每个人部署相同的算法相比，将不同的算法应用于不同的用户可以显著改善流，(ii)个性化分配在更大的数据集中显著改善，(iii)通过优化治疗分配的学习模型可以比优化结果或因果效果预测时增加28%的参与度。
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英文标题：
《A Comparison of Methods for Treatment Assignment with an Application to
  Playlist Generation》
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作者：
Carlos Fern\'andez-Lor\'ia, Foster Provost, Jesse Anderton, Benjamin
  Carterette, Praveen Chandar
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最新提交年份：
2021
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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。
--
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。
--
一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--
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 study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attention from economists, computer scientists, and social scientists. We characterize the various methods proposed in the literature into three general approaches: learning models to predict outcomes, learning models to predict causal effects, and learning models to predict optimal treatment assignments. We show analytically that optimizing for outcome or causal effect prediction is not the same as optimizing for treatment assignments, and thus we should prefer learning models that optimize for treatment assignments. We then compare and contrast the three approaches empirically in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the different treatment assignment approaches on a real-world application at scale (based on more than half a billion individual treatment assignments). Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing for treatment assignment can increase engagement by 28% more than when optimizing for outcome or causal effect predictions.
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PDF链接：
https://arxiv.org/pdf/2004.11532

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关键词：分配方法 econometrics Applications Personalized Multivariate learning approaches 治疗 应用 分配