以因果隨機森林估計分量處置效果

Causal Random Forests with the Instrumental Variable Quantile Regression

項振緯(Chen-Wei Hsiang)

指導教授 ： 陳釗而

共同指導教授 ： 林明仁(Ming-Jen Lin)

國立臺灣大學/社會科學院/經濟學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201901510

摘要

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。

關鍵字

分量處置效果 ； 工具變數 ； 分量迴歸 ； 因果機器學習 ； 隨機森林

並列摘要

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.

並列關鍵字

Quantile treatment effect ； Instrumental variable ； Quantile regression ； Causal machine learning ； Random forest

參考文獻

[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.