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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳釗而(Jau-er Chen) | |
| dc.contributor.author | Chen-Wei Hsiang | en |
| dc.contributor.author | 項振緯 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:38:48Z | - |
| dc.date.copyright | 2019-07-17 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-16 | |
| dc.identifier.citation | [1] Abadie, Alberto, Joshua Angrist, and Guido Imbens. 2002. “Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings.” Econometrica 70(1): 91–117.
[2] Athey, Susan, and Guido Imbens. 2016. “Recursive partitioning for heterogeneous causal effects.” Proceedings of the National Academy of Sciences 113(27): 7353–7360. [3] Athey, Susan, and Guido W Imbens. 2019. “Machine learning methods that economists should know about.” Annual Review of Economics 11. [4] Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. “Generalized random forests.” The Annals of Statistics 47(2): 1148–1178. [5] Athey, Susan, and Stefan Wager. 2018. “Efficient policy learning.” arXiv preprint arXiv:1702.02896v4. [6] Athey, Susan, and Stefan Wager. 2019. “Estimating treatment effects with causal forests: an application.” arXiv preprint arXiv:1902.07409. [7] Bang, Heejung, and James M Robins. 2005. “Doubly robust estimation in missing data and causal inference models.” Biometrics 61(4): 962–973. [8] Breiman, Leo. 2001. “Random forests.” Machine learning 45(1): 5–32. [9] Chen, Jau-Er, and Jia-Jyun Tien. 2019. “Debiased machine learning for instrumental variable quantile regressions.” Working paper. [10] Chen, Le-Yu, and Sokbae Lee. 2018. “Exact computation of GMM estimators for instrumental variable quantile regression models.” Journal of Applied Econometrics 33(4): 553–567. [11] Chen, Yu-Chang. 2018. IVQR: Instrumental variable quantile regression. https://github.com/yuchang0321/IVQR, R package version 0.1.0. [12] 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 21(1): C1–C68. [13] Chernozhukov, Victor, and Christian Hansen. 2004. “The effects of 401 (k) participation on the wealth distribution: an instrumental quantile regression analysis.” Review of Economics and Statistics 86(3): 735–751. [14] Chernozhukov, Victor, and Christian Hansen. 2005. “An IV model of quantile treatment effects.” Econometrica 73(1): 245–261. [15] Chernozhukov, Victor, and Christian Hansen. 2006. “Instrumental quantile regression inference for structural and treatment effect models.” Journal of Econometrics 132(2): 491–525. [16] Chernozhukov, Victor, and Christian Hansen. 2008. “Instrumental variable quantile regression: A robust inference approach.” Journal of Econometrics 142(1): 379–398. [17] Chernozhukov, Victor, and Christian Hansen. 2013. “Summer Institute 2013 Econometric Methods for High-Dimensional Data.” https://www.nber.org/econometrics_minicourse_2013/. [18] Chiou, Yan-Yu, Mei-Yuan Chen, and Jau-er Chen. 2018. “Nonparametric regression with multiple thresholds: Estimation and inference.” Journal of Econometrics 206(2): 472–514. [19] Davis, Jonathan, and Sara B Heller. 2017. “Using causal forests to predict treatment heterogeneity: An application to summer jobs.” American Economic Review 107(5): 546–50. [20] Funk, Michele Jonsson, Daniel Westreich, Chris Wiesen, Til Stürmer, M Alan Brookhart, and Marie Davidian. 2011. “Doubly robust estimation of causal effects.” American Journal of Epidemiology 173(7): 761–767. [21] Gilchrist, Duncan Sheppard, and Emily Glassberg Sands. 2016. “Something to talk about: Social spillovers in movie consumption.” Journal of Political Economy 124(5): 1339–1382. [22] Glynn, Adam N, and Kevin M Quinn. 2010. “An introduction to the augmented inverse propensity weighted estimator.” Political Analysis 18(1): 36–56. [23] Grömping, Ulrike. 2009. “Variable importance assessment in regression: linear regression versus random forest.” The American Statistician 63(4): 308–319. [24] Knaus, Michael, Michael Lechner, and Anthony Strittmatter. 2018. “Machine learning estimation of heterogeneous causal effects: Empirical monte carlo evidence.” arXiv preprint arXiv:1810.13237v2. [25] Louppe, Gilles, Louis Wehenkel, Antonio Sutera, and Pierre Geurts. 2013. “Understanding variable importances in forests of randomized trees.” In Advances in neural information processing systems.: 431–439. [26] O’Neill, Eoghan, and Melvyn Weeks. 2018. “Causal tree estimation of heterogeneous household response to time-of-use electricity pricing schemes.” arXiv preprint arXiv:1810.09179. [27] Robins, James M, Andrea Rotnitzky, and Lue Ping Zhao. 1994. “Estimation of regression coefficients when some regressors are not always observed.” Journal of the American Statistical Association 89(427): 846–866. [28] Scharfstein, Daniel O, Andrea Rotnitzky, and James M Robins. 1999. “Adjusting for nonignorable drop-out using semiparametric nonresponse models.” Journal of the American Statistical Association 94(448): 1096–1120. [29] Tibshirani, Julie, Susan Athey, Stefan Wager, Rina Friedberg, Luke Miner, and Marvin Wright. 2018. grf: Generalized random forests (beta). https://CRAN.R-project.org/package=grf, R package version 0.10.2. [30] Wager, Stefan, and Susan Athey. 2018. “Estimation and inference of heterogeneous treatment effects using random forests.” Journal of the American Statistical Association 113(523): 1228–1242. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21585 | - |
| dc.description.abstract | 根據 Athey、Tibshirani、與 Wager (2019, The Annals of Statistics) 所建構的一般化隨機森林架構，本文探討如何以因果機器學習的方法估計工具變數分量迴歸。我們提出的計量方法能無母數地估計分量處置效果，並且衡量各個控制變數在異質性上的重要性。本文也依據此計量方法重新檢視兩個實證研究: 401(k) 退休金制度對財富的處置效果、以及職業訓練對所得的影響。 | zh_TW |
| dc.description.abstract | We propose an econometric procedure based mainly on the generalized random forests of Athey, Tibshirani and Wager (2019, The Annals of Statistics). Not only estimates the conditional quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile effect of participating a job training program on earnings. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:38:48Z (GMT). No. of bitstreams: 1 ntu-108-R06323026-1.pdf: 945062 bytes, checksum: 490cf36c552b995d3c16ee1437a1f05b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 1 Introduction ...1
2 The Model and Algorithm ...3 2.1 Generalized Random Forests ...3 2.2 Tree Splitting Rules ...5 2.3 The Algorithm and an Example Illustrating Weights Calculation ...9 2.4 Practical Implementation ...11 3 Variable Importance ...13 4 Empirical Studies ...14 4.1 The 401(k) Retirement Savings Plan ...14 4.2 The Job Training Program ...17 5 Conclusion ...23 References ...24 Appendix A. Improving Efficiency by Doubly Robust Estimators ...26 A.1 The Doubly Robust Estimation for Causal Forests ...26 A.2 The Doubly Robust Estimation for Instrumental Causal Forests ...28 A.3 An Unsolved Task: The Doubly Robust GRF-IVQR ...29 | |
| dc.language.iso | en | |
| dc.title | 以因果隨機森林估計分量處置效果 | zh_TW |
| dc.title | Causal Random Forests with the Instrumental Variable Quantile Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 林明仁(Ming-Jen Lin) | |
| dc.contributor.oralexamcommittee | 狄萊(Patrick DeJarnette) | |
| dc.subject.keyword | 分量處置效果,工具變數,分量迴歸,因果機器學習,隨機森林, | zh_TW |
| dc.subject.keyword | Quantile treatment effect,Instrumental variable,Quantile regression,Causal machine learning,Random forest, | en |
| dc.relation.page | 29 | |
| dc.identifier.doi | 10.6342/NTU201901510 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-07-16 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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