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Causal Random Forests Model Using Instrumental Variable Quantile Regression

Author

Listed:
  • Jau-er Chen

    (Institute for International Strategy, Tokyo International University, 1-13-1 Matobakita Kawagoe, Saitama 350-1197, Japan
    Center for Research in Econometric Theory and Applications, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan)

  • Chen-Wei Hsiang

    (Behavioral and Data Science Research Center, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan)

Abstract

We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the 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 earnings effect of participating in a job training program.

Suggested Citation

  • Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:4:p:49-:d:298392
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    References listed on IDEAS

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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Brigham R. Frandsen & Lars J. Lefgren, 2018. "Testing Rank Similarity," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 86-91, March.
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    4. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    5. Duncan Sheppard Gilchrist & Emily Glassberg Sands, 2016. "Something to Talk About: Social Spillovers in Movie Consumption," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1339-1382.
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    13. Yan-Yu Chiou & Mei-Yuan Chen & Jau-er Chen, 2017. "Nonparametric Regression with Multiple Thresholds: Estimation and Inference," Papers 1705.09418, arXiv.org, revised Feb 2018.
    14. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
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    Cited by:

    1. Alessio Abeltino & Andrea Pannone & Andrea Bernardini, 2026. "Speculation and retail price transmission in the frozen concentrated orange juice market: a causal machine learning analysis," Future Business Journal, Springer, vol. 12(1), pages 1-15, December.
    2. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    3. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    4. Emre Tepe, 2024. "A random forests-based hedonic price model accounting for spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 26(4), pages 511-540, October.
    5. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.

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