Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations (Journal of Economerics 2020). • Doudchenko, Khosravi, Pouget-Abadie, Lahaie, Lubin, Mirrokni, Spiess, Imbens. Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls (NeurIPS 2021)
Leyton-Brown, and Taddy (ICML 2017) n KernelIV: Singh, Sahani,, and Gretton (NeurIPS 2019) n DFIV: Xu, Chen, Srinivasan, de Freitas, Doucet, and Gretton (ICLR 2021) 機械学習によるアプローチ 18
An Empiricist’s Companion, Princeton University Press. • Bennett, A., Kallus, N., and Schnabel, T. (2019), “Deep Generalized Method of Moments for Instrumental Variable Analysis,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., vol. 32. • Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J.(2018), “Double/debiased machine learning for treatment and structural parameters,”Econometrics Journal, 21, C1–C68. • Chernozhukov, V., Newey, W., Singh, R., and Syrgkanis, V. (2020), “Adversarial Estimation of Riesz Representers,” . • Dikkala, N., Lewis, G., Mackey, L., and Syrgkanis, V. (2020), “Minimax Estimation of Conditional Moment Models,” in Advances in Neural Information Processing Systems, Curran Associates, Inc.,vol. 33, pp. 12248–12262. • Good, I. J. and Gaskins, R. A. (1971), “Nonparametric Roughness Penalties for Probability Densities,”Biometrika, 58, 255– 277. 47
(2017), “Deep IV: A Flexible Approach for Counterfactual Prediction,” in Proceedings of the 34th International Conference on Machine Learning ,PMLR, vol. 70 of Proceedings of Machine Learning Research, pp. 1414–1423. • Imbens, G. W. (1997), “One-Step Estimators for Over-Identified Generalized Method of Moments Models,”The Review of Economic Studies, 64, 359–383. • Kanamori, T., Hido, S., and Sugiyama, M. (2009), A least-squares approach to directimportance estimation.Journal of Machine Learning Research, 10(Jul.):1391–1445. • Kato, M., Imaizumi, M., McAlinn, K., Yasui, S., and Kakehi, H. (2022a), “Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting,” in International Conference on Learning Representations. • Kato, M., Imaizumi, M., and Minami, K. (2022b), “Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics,” . • Shimodaira, H. (2000), “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of Statistical Planning and Inference, 90, 227–244. 48
Instrumental Variable Regression,” in Advances in Neural InformationProcessing Systems, Curran Associates, Inc., vol. 32. • Sugiyama, M., Suzuki, T., and Kanamori, T. (2011), “Density Ratio Matching under the Bregman Divergence: A Unified Frameworkof Density Ratio Estimation,”Annals of the Institute of Statistical Mathematics, 64.— (2012), Density Ratio Estimation in Machine Learning, New York, NY, USA: Cambridge University Press, 1st ed. • Sugiyama. M. (2006), Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. J. Mach. Learn. Res. 7 (12/1/2006), 141–166. • Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P., and Kawanabe, M. (2007). Direct importance estimation with model selection and its application to covariate shift adaptation. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS'07). Curran Associates Inc., Red Hook, NY, USA, 1433–1440. • Suzuki, T., Sugiyama, M., Sese, Jun., and Kanamori, T. (2008). Approximating mutual information by maximum likelihood density ratio estimation. In Proceedings of the Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery at ECML/PKDD 2008,volume 4 of Proceedings of Machine Learning Research, pp. 5–20. PMLR. 49
restriction models with unknown functions. Econometric Theory, 27(1):8–46. • Wooldridge, J. M. (2002),Econometric analysis of cross section and panel data, MIT Press.— (2009), Introductory Econometrics: A Modern Approach, ISE - International Student Edition, South-Western. • Xu, L., Chen, Y., Srinivasan, S., de Freitas, N., Doucet, A., and Gretton, A. (2021), “Learning Deep Features in Instrumental Variable Regression,” in International Conference on Learning Representations. • Zheng, W. and van der Laan, M. J. (2011), “Cross-Validated Targeted Minimum-Loss-Based Estimation,” in Targeted Learning: Causal Inference for Observational and Experimental Data. 50