Author(s)

Zongxuan Chai ¹, Tingting Zheng ¹

Affiliation(s)

¹ School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, China

Corresponding Author

Tingting Zheng

ABSTRACT

This paper focuses on the differences in the application of measurement and machine learning methods when making causal inferences. Newly highlighted double machine learning offers new research methods for policy or intervention evaluation in economic research panel data interpretability remains in doubt. We select the county panel data of Fujian Province, establish a quasi-natural experiment, and adopt a general double machine learning model and a differences-in-differences (DID) model to evaluate the policy effect of the new town policy on the optimisation of industrial structure, respectively. Both results demonstrate that the new town policy has a significant optimisation impact on industrial structure, but the dual machine learning results differ under this sample by the influence of the algorithm's advance selection. The stability test proves that the DID is more stable, and the economic significance is more explained, which is contrary to the premise of the universality of double machine learning.

KEYWORDS

Double machine learning; Differences-in-Differences; Machine learning explanation

CITE THIS PAPER

Zongxuan Chai, Tingting Zheng, The Explaimability of Double Machine Learning Causal Inference in Quasi-Natural Experiments—A Study Based on County Panel Sample Data. Automation and Machine Learning (2023) Vol. 4: 49-54. DOI: http://dx.doi.org/10.23977/autml.2023.040306.

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