高維度工具變數分量迴歸架構下的因果機器學習方法

Debiased Machine Learning for Instrumental Variable Quantile Regressions

田家駿(Jia-Jyun Tien)

指導教授 ： 陳釗而

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/NTU201902202

摘要

在高維度控制變數的工具變數分量迴歸架構下，本文討論如何使用雙重機器學習 (Double Machine Learning) 技法估計低維度的分量處置效果。此估計與檢定步驟滿足文獻裡所提出的黎曼型式的正交條件 (Neyman-type Orthogonality Condition)。蒙地卡羅模擬結果顯示本文所提出的方法在高維度架構下比傳統工具變數分量迴歸的估計更俱效率性。我們也將此方法應用在重新檢視兩個實證例子: 參加401(k)退休金制度對累積財富的影響，以及失業後參加職業訓練對未來所得的影響。

關鍵字

工具變數 ； 分量迴歸 ； 分量處置效果 ； LASSO ； 因果機器學習

並列摘要

The aim of this paper is to investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. The estimation and inference are based on the Neyman-type orthogonal moment conditions, that are relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the econometric procedure performs well. We also apply the procedure to reinvestigate two empirical studies:the quantile treatment effect of 401(k) participation on accumulated wealth, and the distributional effect of job-training program participation on trainee earnings.

並列關鍵字

Instrumental Variable ； Quantile Regression ； Treatment Effect ； LASSO ； Double Machine Learning

參考文獻

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