計量経済学と機械学習の関係 ‒AI はさだめ, さ だめは反事実的‒ Relationship between Econometrics and Machine Learning: AI is the Plan, the Plan is Counterfactual @ill-identified 2019/7/27, Updated: 2020/2/5 1
[1] Angrist, Joshua D. (1990) “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records,” The American Economic Review, Vol. 80, No. 3, pp. 313–336, retrieved from here. [2] Angrist, Joshua D. and Victor Lavy (1999) “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement,” Quarterly Journal of Economics, Vol. 114, No. 2, pp. 533–575. [3] Angrist, Joshua D and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics: An Empiricist’s Companion: Princeton University Press, retrieved from here, （大森義明‧小原美紀‧田中隆一‧野口晴子訳， 『ほとんど無害な計量経済学 –応用経済学のための実証分析ガイド–』 ， NTT 出版，2013 年）. [4] Ashenfelter, Orley and Cecilia Rouse (1998) “Income, Schooling, and Ability: Evidence from a New Sample of Identical Twins,” The Quarterly Journal of Economics, Vol. 113, No. 1, pp. 253–284, February, DOI: 10.1162/003355398555577.
[5] Athey, Susan (2017) “Beyond Prediction: Using Big Data for Policy Problems,” Science, Vol. 355, No. 6324, pp. 483–485, February, DOI: 10.1126/science.aal4321. [6] (2018) “The Impact of Machine Learning on Economics,” in The Economics of Artificial Intelligence: An Agenda: University of Chicago Press, pp. 507–547, retrieved from here. [7] Athey, Susan, Julie Tibshirani, and Stefan Wager (2019) “Generalized Random Forests,” The Annals of Statistics, Vol. 47, No. 2, pp. 1148–1178, April, DOI: 10.1214/18-AOS1709. [8] Barocas, Solon, Moritz Hardt, and Arvind Narayanan (2018) Fairness and Machine Learning: fairmlbook.org, retrieved from here. [9] Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott (2015) “Inferring Causal Impact Using Bayesian Structural Time-Series Models,” The Annals of Applied Statistics, Vol. 9, No. 1, pp. 247–274, March, DOI: 10.1214/14-AOAS788.
[10] Campbell, Murray, A.Joseph Hoane, and Feng-hsiung Hsu (2002) “Deep Blue,” Artificial Intelligence, Vol. 134, No. 1-2, pp. 57–83, January, DOI: 10.1016/S0004-3702(01)00129-1. [11] Card, David and Alan B. Krueger (1994) “Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania,” American Economic Review, Vol. 84, No. 4, pp. 772–793, retrieved from here, NBER Working Paper Version: 10.3386/w4509. [12] Chernozhukov, Victor and Christian Hansen (2005) “An IV Model of Quantile Treatment Effects,” Econometrica, Vol. 73, No. 1, pp. 245–261, January, DOI: 10.1111/j.1468-0262.2005.00570.x. [13] Chernozhukov, Victor, Han Hong, and Elie Tamer (2007) “Estimation and Confidence Regions for Parameter Sets in Econometric Models,” Econometrica, Vol. 75, No. 5, pp. 1243–1284, September, DOI: 10.1111/j.1468-0262.2007.00794.x.
[14] Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey (2017) “Double/Debiased/Neyman Machine Learning of Treatment Effects,” American Economic Review, Vol. 107, No. 5, pp. 261–265, May, DOI: 10.1257/aer.p20171038. [15] Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins (2018) “Double/Debiased Machine Learning for Treatment and Structural Parameters,” The Econometrics Journal, Vol. 21, No. 1, pp. C1–C68, February, DOI: 10.1111/ectj.12097. [16] Cybenko, George (1989) “Approximation by Superpositions of a Sigmoidal Function,” Mathematics of Control, Signals, and Systems, Vol. 2, No. 4, pp. 303–314, December, DOI: 10.1007/BF02551274. [17] Deaton, Angus and Nancy Cartwright (2018) “Understanding and Misunderstanding Randomized Controlled Trials,” Social Science & Medicine, Vol. 210, pp. 2–21, August, DOI: 10.1016/j.socscimed.2017.12.005, NBER working paper version: 10.3386/w22595.
[18] Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel (2012) “Fairness through Awareness,” in Proceedings of the 3rd Innovations in Theoretical Computer Science Conference on - ITCS ’12, pp. 214–226, Cambridge, Massachusetts: ACM Press, DOI: 10.1145/2090236.2090255. [19] Harrel, Frank (2018) “Road Map for Choosing Between Statistical Modeling and Machine Learning,” September, retrieved from here, 和 訳: ⻄田勘一郎『統計のモデルと機械学習のモデル、どう使い分ければ よいのか』. [20] Hernán, Miguel A., John Hsu, and Brian Healy (2019) “A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks,” CHANCE, Vol. 32, No. 1, pp. 42–49, January, DOI: 10.1080/09332480.2019.1579578, ⻄田勘一郎による要約: 『予測と因果 関係は何が違うのか - Part １』 『予測と因果関係 - Part 2: 予測は自動化 できても因果推論は自動化できない』.
[21] Hornik, Kurt, Maxwell Stinchcombe, and Halbert White (1989) “Multilayer Feedforward Networks Are Universal Approximators,” Neural Networks, Vol. 2, No. 5, pp. 359–366, January, DOI: 10.1016/0893-6080(89)90020-8. [22] Hotz, V. J., R. A. Miller, S. Sanders, and J. Smith (1994) “A Simulation Estimator for Dynamic Models of Discrete Choice,” The Review of Economic Studies, Vol. 61, No. 2, pp. 265–289, April, DOI: 10.2307/2297981. [23] Hotz, V Joseph and Robert A. Miller (1993) “Conditional Choice Probabilities and the Estimation of Dynamic Models,” Review of Economic Studies, Vol. 60, No. 3, pp. 497–529, DOI: 10.2307/2298122. [24] Igami, Mitsuru (2018) “Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo,” March, arXiv: 1710.10967. [25] Imbens, Guido (2018a) “Understanding and Misunderstanding Randomized Controlled Trials: A Commentary on Deaton and Cartwright,” Social Science & Medicine, Vol. 210, pp. 50–52, August, DOI: 10.1016/j.socscimed.2018.04.028, working paper version: here.
[26] (2018b) “Causal Inference and Machine Learning,” June, retrieved from here. [27] Imbens, Guido W. (2019) “Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics,” arXiv:1907.07271 [stat], July, arXiv: 1907.07271. [28] LaLonde, Robert J. (1986) “Evaluating the Econometric Evaluations of Training Programs with Experimental Data,” American Economic Review, Vol. 75, No. 4, pp. 604–620, retrieved from here. [29] Mullainathan, Sendhil and Jann Spiess (2017) “Machine Learning: An Applied Econometric Approach,” Journal of Economic Perspectives, Vol. 31, No. 2, pp. 87–106, May, DOI: 10.1257/jep.31.2.87. [30] Pearl, Judea (2019) “The Seven Tools of Causal Inference, with Reflections on Machine Learning,” Communications of the ACM, Vol. 62, No. 3, pp. 54–60, February, DOI: 10.1145/3241036. [31] Rubin, Donald B. (1974) “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.,” Journal of Educational Psychology, Vol. 66, No. 5, pp. 688–701, DOI: 10.1037/h0037350.
[32] (1990) “Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies,” Statistical Science, Vol. 5, No. 4, pp. 472–480, November, DOI: 10.1214/ss/1177012032, On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9. [33] Rust, John (1987) “Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher,” Econometrica, Vol. 55, No. 5, p. 999, September, DOI: 10.2307/1911259. [34] Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis (2016) “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, Vol. 529, No. 7587, pp. 484–489, January, DOI: 10.1038/nature16961.
[35] Varian, Hal R. (2014) “Big Data: New Tricks for Econometrics,” Journal of Economic Perspectives, Vol. 28, No. 2, pp. 3–28, May, DOI: 10.1257/jep.28.2.3. [36] Wager, Stefan and Susan Athey (2018) “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests,” Journal of the American Statistical Association, Vol. 113, No. 523, pp. 1228–1242, July, DOI: 10.1080/01621459.2017.1319839. [37] 神嶌敏弘 (2017) 「公平配慮型データマイニング技術の進展」 ， 『第 31 回人工知能学会全国大会論文集』 ，一般社団法人人工知能学会，DOI： 10.11517/pjsai.JSAI2017.0_1E1OS24a1． [38] 清水昌平 (2016) 「因果探索: 基本から最近の発展までを概説」 ，5 月, retrieved from here． [39] (2017a) 「統計的因果推論への招待 -因果構造探索を中心に-」 ， 7 月, retrieved from here． [40] (2017b) 『統計的因果探索』 ，機械学習プロフェッショナルシリ ーズ，講談社，東京都文京区, retrieved from here．
ktgrstsh
ssii
dscs
misawann
mlarrieu
carlislerainey
lcolladotor
ianozsvald
eureco
koro485
addyosmani
lara
marcduiker
davidbonilla
reverentgeek
jasonvnalue
shpigford
scottboms
samlambert
philhawksworth
mthomps