QJE

Human Decisions and Machine Predictions

https://academic.oup.com/qje/article-abstract/doi/10.1093/qje/qjx032/4095198/Human-Decisions-and-Machine-Predictions?redirectedFrom=fulltext&login=false

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

https://jmlr.org/papers/volume14/bottou13a/bottou13a.pdf

因果推断领域：

1. Judea Pearl 的 do-calculus，用概率图模型进行因果推断。

Pearl, J. (2012). The do-calculus revisited. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (pp. 3-11).

2. Johansson et al (2016) 用 representation learning进行 Rubin 框架下的反事实推断。

Johansson, F., Shalit, U., & Sontag, D. (2016). Learning representations for counterfactual inference. In

International conference on machine learning (pp. 3020-3029). PMLR.

3. Jason Hartford et al (2017) 的 DeepIV，用deep learning 来寻找工具变量进行反事实推断研究。

Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning, 2017.

4. Susan Athey 的 causal tree 模型，用树模型进行因果推断。

Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects.

Proceedings of the National Academy of Sciences, 113 (27), 7353-7360.

行为经济学领域：

1. Param Singh et al (2011) 用了 hidden markov model，来研究用户的隐含状态对可观测行为的影响。

Singh, P. V., Tan, Y., & Youn, N. (2011). A hidden Markov model of developer learning dynamics in open source software projects. Information Systems Research, 22(4), 790-807.

2. Jacobs et al (2016) 用了 Topic Modeling，来预测用户在线购买商品的行为。

Jacobs, B. J., Donkers, B., & Fok, D. (2016). Model-based purchase predictions for large assortments. Marketing Science,35(3), 389-404.

3. Peter Fader 的 Pareto/NBD model，用统计学模型预测和估计顾客的终身价值。

Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing science,24 (2), 275-284.

4. Peter Hoff 的 Latent Space Model，用统计学模型估计网络结构上用户的传播信息行为。

Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis.

5. Bayesian Learning，很多经济学、金融学、营销学的TOP论文都讨论过类似于 Customer Learning， Investor Learning 的问题。

Chance, D. M., Hillebrand, E., & Hilliard, J. E. (2008). Pricing an option on revenue from an innovation: An application to movie box office revenue. Management Science,54(5), 1015-1028.

Narayan, V., Rao, V. R., & Saunders, C. (2011). How peer influence affects

attribute preferences: A Bayesian updating mechanism.Marketing Science,30(2), 368-384.

Kwon, H. D., & Lippman, S. A. (2011). Acquisition of project-specific assets with Bayesian updating.

Operations research,59(5), 1119-1130.

博弈论领域：

Reinforcement Learning 和 GAN天然就是用来研究博弈论的，比如 Igami 教授论述了动态离散选择模型与 Reinforcement Learning 与我们已知的一些人工智能算法间的关系。这个方向在工程学里一直有人研究。像什么演化博弈、Agent model 等等

Igami, M. (2020). Artificial intelligence as structural

estimation: Deep Blue, Bonanza, andAlphaGo.The Econometrics Journal,23(3), S1-S24.

Charpentier, A., Elie, R., & Remlinger, C. (2021). Reinforcement learning in economics and finance.

Computational Economics, 1-38.

挖掘新的自变量：

1. Netzer et al (2012) 用 text ming 的方法，挖掘厂商之间的竞争关系是市场份额

Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science,31(3), 521-543.

2.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。

Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2021). What makes a good image? Airbnb demand analytics leveraging interpretable image features.Management Science.

3.Liu et al (2019) 用 video mining 的方法，研究 youtube 视频对于患者康复的影响

Liu, X., Zhang, B., Susarla, A., & Padman, R. (2019). Go to YouTube and call me in the morning: use of social media for chronic conditions.MIS Quarterly.

业界正在做的项目：

google 的 What-If 项目: what-if-tool

很多大厂的推荐算法：讨论推荐算法是否公平的问题， fairness of recommendation algorithms

Reference

https://www.zhihu.com/question/37870042