例如，搜索引擎关键词广告竞价拍卖的巨大商业价值催生了一批非常高质量的交叉学科研究和学术社区。Nekipelov et al. (2015) 研究了一个非常经典的拍卖实证问题：如何从卖拍的报价数据推断竞拍者对拍卖标的的真实估值。但是作者并不依赖拍卖结果是纳什均衡的假设，而假设竞拍者是不断地在学习最好的竞拍策略。这对于搜索引擎关键词竞价拍卖这个高频率并且拍卖利益巨大应用场景是很贴切的。
3. 前沿文献汇总
答主「meta318」总结了部分近年来经济学不同领域应用机器学习的有关文献：
3.1 因果推断领域
Pearl (2012) 用概率图模型进行因果推断。
Johansson et al. (2016) 用 representation learning 进行 Rubin 框架下的反事实推断。
Hartford et al. (2017) 用 deep learning 来寻找工具变量进行反事实推断研究。
Athey and Imbens (2016) 用树模型进行因果推断。
3.2 行为经济学领域
Singh et al. (2011) 用 hidden markov model 来研究用户的隐含状态对可观测行为的影响。
Jacobs et al. (2016) 用 Topic Modeling 来预测用户在线购买商品的行为。
Fader et al. (2005) 用统计学模型预测和估计顾客的终身价值。
Hoff et al. (2002) 用统计学模型估计网络结构上用户的传播信息行为。
3.3 博弈论领域
Igami (2020) 阐述了人工智能的某些算法与动态结构模型的计量经济学之间的联系。
Charpentier et al. (2021) 介绍了强化学习在经济学、博弈论、运筹学和金融学方面的应用。
3.4 挖掘新的自变量
Netzer et al. (2012) 用文本挖掘的方法分析企业所处的市场结构和竞争格局。
Zhang et al. (2021) 用 image mining 的方法，研究 Airbnb 上房间图片质量与用户需求之间的关系。
Liu et al. (2019) 用 video mining 的方法，研究 YouTube 视频对于患者康复的影响。
Athey, Susan, and Guido Imbens. 2016. “Recursive Partitioning for Heterogeneous Causal Effects.” Proceedings of the National Academy of Sciences of the United States of America 113 (27): 7353–60.
Burgess, R., M. Hansen, and B. A. Olken. 2012. “The Political Economy of Deforestation in the Tropics.” Journal of Economics. https://academic.oup.com/qje/article-abstract/127/4/1707/1844248.
Charpentier, Arthur, Romuald Élie, and Carl Remlinger. 2021. “Reinforcement Learning in Economics and Finance.” Computational Economics, April. https://doi.org/10.1007/s10614-021-10119-4.
Fader, Peter S., Bruce G. S. Hardie, and Ka Lok Lee. 2005. “‘Counting Your Customers’ the Easy Way: An Alternative to the Pareto/NBD Model.” Marketing Science 24 (2): 275–84.
Hartford, Jason, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 06--11 Aug 2017. “Deep IV: A Flexible Approach for Counterfactual Prediction.” In Proceedings of the 34th International Conference on Machine Learning, edited by Doina Precup and Yee Whye Teh, 70:1414–23. Proceedings of Machine Learning Research. PMLR.
Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. 2002. “Latent Space Approaches to Social Network Analysis.” Journal of the American Statistical Association 97 (460): 1090–98.
Igami, Mitsuru. 2020. “Artificial Intelligence as Structural Estimation: Deep Blue, Bonanza, and AlphaGo.” The Econometrics Journal 23 (3): S1–24.
Jacobs, Bruno J. D., Bas Donkers, and Dennis Fok. 2016. “Model-Based Purchase Predictions for Large Assortments.” Marketing Science 35 (3): 389–404.
Johansson, F., and U. Shalit. 2016. “Learning Representations for Counterfactual Inference.” Conference on Machine …. http://proceedings.mlr.press/v48/johansson16.html.
Liu, Xiao, Bin Zhang, Anjana Susarla, and Rema Padman. 2019. “Go to YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions.” Forthcoming, MIS Quarterly. https://doi.org/10.2139/ssrn.3061149.
Nekipelov, Denis, Vasilis Syrgkanis, and Eva Tardos. 2015. “Econometrics for Learning Agents.” In Proceedings of the Sixteenth ACM Conference on Economics and Computation, 1–18. EC ’15. New York, NY, USA: Association for Computing Machinery.
Netzer, Oded, Ronen Feldman, Jacob Goldenberg, and Moshe Fresko. 2012. “Mine Your Own Business: Market-Structure Surveillance Through Text Mining.” Marketing Science 31 (3): 521–43.
Pearl, Judea. 2012. “The Do-Calculus Revisited.” arXiv [cs.AI]. arXiv. http://arxiv.org/abs/1210.4852.
Singh, Param Vir, Yong Tan, and Nara Youn. 2011. “A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects.” Information Systems Research 22 (4): 790–807.
Varian, Hal R. 2014. “Big Data: New Tricks for Econometrics.” The Journal of Economic Perspectives: A Journal of the American Economic Association 28 (2): 3–28.
Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan. 2021. “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.” Management Science, December. https://doi.org/10.1287/mnsc.2021.4175.
