東京国際大学 E-Track Degree Program ビジネスエコノミクス専攻
- 博士前期課程: 経済学における機械学習 (Machine Learning in Economics)
- 学部課程: 経済学における機械学習 (Machine Learning in Economics)
2020年から2024年には日本学術振興会から科研費を得て、研究課題番号20K01593(基盤C)の「因果機械学習に基づく分位点処置効果の計量解析とその経済学における応用」に取り組んでいる。
♛Introduction – ML in Econ; ♝Causal inference flowchart; ♜Introduction – The importance of being causal (Harvard Data Science Review)
My Github current projects:
Computational applications to behavioral science [Stanford GSB]
Causal machine learning and ‘Metrics, slides, Stanford GSB
Double/Debiased machine learning, MIT Econ
(Causal) machine learning for healthcare – lecture notes video, MIT
Learning from Data, Caltech
ML cheatsheets, Stanford CS
Webcasts
- Varian (Chief Economist, Google): Do good data – Causal inference meets big data
- *Athey, Susan (Stanford GSB): The Impact of Machine Learning on Econometrics and Economics
- *Athey, Susan (Stanford GSB): Counterfactual Inference (NeurIPS 2018 Tutorial)
- *Alexandre Belloni (Duke): High-dimensional Econometrics – from foundations to econometric models
- Martin Wainwright (UC Berkeley): High-dimensional Statistics
- Mullainathan (Harvard): Machine Learning and Prediction in Economics and Finance
- Tom Mitchell (CMU): Machine Learning
- Larry Wasserman (CMU): Machine Learning and Econometrics: Inference
- Fridman (MIT): Deep learning basics: intro and overview
- Alexander Amini (MIT): 6.S191 Introduction to Deep Learning. [webcasts]
- Percy Liang and Dorsa Sadigh (Stanford): CS221 – Artificial Intelligence: Principles and Techniques
Textbooks
- *Business Data Science: combining machine learning and economics to optimize, automate, and accelerate business decisions
- *The Elements of Statistical Learning
- *High-Dimensional Statistics – a non-asymptotic viewpoint
- An introduction to statistical learning with applications in R
- Understanding machine learning – From theory to algorithms
- Learning from data
Readings
- Athey, S. 2017. “Beyond prediction: Using big data for policy problems,” Science.
- Lazer et al. 2014. “The Parable of Google Flu: Traps in Big Data Analysis,” Science.
- Athey, S. 2018. “The Impact of Machine Learning on Economics,” working paper, Stanford GSB.
- *Athey and Imbens. 2019. “Machine Learning Methods Economists Should Know about,” Annual Review of Economics
- *Belloni A., Chernozhukov V., and Hansen C. 2014. “High-Dimensional Methods and Inference on Structural and Treatment Effects,” Journal of Economic Perspectives, Vol. 28, No. 2, pp. 29-50.
- Mullainathan S. and Spiess J. 2017. “Machine Learning: An Applied Econometric Approach,” Journal of Economic Perspectives, Vol. 31, No. 2, pp. 87-106.
- Varian, H.R. 2014. “Big Data: New Tricks for Econometrics,” Journal of Economic Perspectives, Vol. 28, No. 2, pp. 3-28.
- *Empirical Asset Pricing via Machine Learning, SSRN.
-
Taming the Factor Zoo: A Test of New Factors, Journal of Finance. 2020.
- *Autoencoder Asset Pricing Models, Journal of Econometrics. 2020.
- Machine Learning Estimation of heterogeneous Causal Effects: Empirical Monte Carlo Evidence, axXiv.econometrics
- *Kanus, Lechner, and Strittmatter (2020). “Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach,” Journal of Human Resources.
- *Angrist and Frandsen (2020). “Machine Labor,” NBER paper.
- *Iskhakov, Rust, and Schjerning (2020). “Machine Learning and Structural Econometrics: Contrasts and Synergies,” The Econometrics Journal.
- Igami, M. 2020. Artificial Intelligence as Structural Estimation: Deep Blue, Bonanza, and AlphaGo, The Econometrics Journal.
R programming
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