Xia Wang
ABSTRACT
With the development of artificial intelligence, big data technology and digital economy, machine learning is widely used in the fields of social economy. This paper introduces double machine learning method for estimation of heterogeneous treatment effects and make an experiment to test its application in economics on an economic example. Using the linear and causal forest algorithms of machine learning, we estimate the parametric and nonparametric models of treatment effects. The results of the experiment indicate that the estimation results of double machine learning method for treatment effects are significant and effective. The fitting results of both parametric and nonparametric models are in line with expectations. Nonparametric model is fitted better. The results of machine learning method are more accurate and the loss is less.
- Davis J M V, Heller S B. 2020. Rethinking the benefits of youth employment programs: The heterogeneous effects of summer jobs[J]. The Review of Economics and Statistics, 102(4):664-677.Google Scholar
Cross Ref
- Knittel C R, Stolper S. 2019. Using machine learning to target treatment: The case of Household energy use[ R/OL]. NBER Working Paper No. 26531, http://www.nber. org/ papers/ w26531.Google Scholar
- Wager S, Athey S. 2018. Estimation and inference of heterogeneous treatment effects using random forests[J]. Journal of the American Statistical Association, 113(523):1228-1242.Google ScholarCross Ref
- Chernozhukov, V., Chetverikov, D., Demirer, M.,Duflo, E., Hansen, C., Newey, W., & Robins, J. 2018. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.Google ScholarCross Ref
- V. Chernozhukov, M. Goldman, V. Semenova, and M. Taddy. 2017. Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels.Google Scholar
- V. Chernozhukov, D. Nekipelov, V. Semenova, and V. Syrgkanis. 2018. Two-Stage Estimation with a High-Dimensional Second Stage.Google Scholar
- Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 2017.Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning.Google Scholar
- S. Athey, J. Tibshirani and S. Wager. 2019. Generalized Random Forests. Annals of Statistics.Google ScholarCross Ref
- C.Tran and E.Zheleva. 2019. “Learning triggers for heterogeneous treatment effects,” in Proceedings of the AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Hu Zunguo, Xiong Yunhui, Deng Lijie, Peng Xinyu. 2022.Re-evaluation of the effect of regional coordinated development policy – analysis of the effect of dealing with heterogeneity from cal forest algorithm [J]. Journal of Economics.9(02):201-235.Google Scholar
- Wan Yongkun, Wang Chenchen. 2022.An empirical test of the high-quality development enabled by the digital economy [J]. Statistics and decision making, 38(04): 21-26.Google Scholar
- Hill, J, A. Linero & J. Murray 2020, “Bayesian Additive Regression Trees: A Review and Look Forward.” Annual Review of Statistics and Its Application 7(1).Google ScholarCross Ref
- Kolasa & Steward T. A. Pickett(eds.). Ecological Heterogeneity. New York, NY: Springer.Google Scholar
- Molnar, Christoph 2020, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.Google Scholar
- Https://Christophm.Github.Io/Interpretable−M1−Book/.Google Scholar
- Hainmueller, J., J. Mummolo & Y. Xu 2019. “How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice.” Political Analysis 27(2).Google Scholar
- Lewis, G., Syrgkanis, V. 2021. Double/Debiased Machine Learning for Dynamic Treatment Effects. https://arxiv.org/abs/2002.07285Google Scholar
- Ren, Q.G., Zhang, G., Yue, X.G., Liao, W.C. Deep foundation pit monitoring based on CX-3C inclinometer (2014) Applied Mechanics and Materials, 484-485, pp. 404-407. DOI: 10.4028/www.scientific.net/AMM.484-485.404Google Scholar
- Yuan, D.C., Yue, X.G., Wang, C., Zhang, J.F. Gas emission prediction based on coal mine operating data (2014) Applied Mechanics and Materials, 484-485, pp. 604-607. DOI: 10.4028/www.scientific.net/AMM.484-485.604Google Scholar
- Yuan, D.C., Yue, X.G. SVMR model for coal mine cost management prediction (2014) Applied Mechanics and Materials, 484-485, pp. 608-611. DOI: 10.4028/www.scientific.net/AMM.484-485.608Google Scholar
- Zhang, G., Yue, X.-G., Li, M.-X., Chen, J.-X. Biological opposite degree algorithm and its application in coal and gas outburst prediction (2014) Journal of Chemical and Pharmaceutical Research, 6 (6), pp. 1756-1761.Google Scholar
- Guo, H.B., Yue, X.G., Chen, X.W. Study of fuzzy mathematics in diseases diagnosis (2014) Applied Mechanics and Materials, 608-609, pp. 835-838. DOI: 10.4028/www.scientific.net/AMM.608-609.835Google Scholar
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