The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations

Yangyang Liu

doi:10.47297/wspceWSP2516-251919.20261002

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The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations

10卷第2期

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The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations
The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations
The Williams School New London Connecticut, London, 06320, UK
2026年10卷第2期页码：94-97
纸质出版：2026-04-30
DOI：10.47297/wspceWSP2516-251919.20261002
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Yangyang Liu. (2026). The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations. 创意经济（英文版）, 10(2), 94-97.

Liu Yangyang.The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations[J].Creative Economy,2026,10(02):94-97.
Yangyang Liu. (2026). The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations. 创意经济（英文版）, 10(2), 94-97. DOI： 10.47297/wspceWSP2516-251919.20261002.

Liu Yangyang.The Application of Machine Learning in Causal Inference for Economic Policy Evaluation: Opportunities, Challenges, and Limitations[J].Creative Economy,2026,10(02):94-97. DOI： 10.47297/wspceWSP2516-251919.20261002.

摘要

Abstract

This paper discusses the integration of machine learning (ML) approach into the causal influence framework for economic policy evaluation

the focus is on estimate in the average treatment effects (ATE) and heterogeneous treatment effects (HTE) as part of the observational settings. Economic policies rely more on the administrative data that is at high dimensional

which could include welfare programs

labor market interventions

and industrial subsidies. Under such condition

traditional econometric approaches face many challenges that including the nonlinear interactions

model misspecification

and treatment heterogeneity. There are recent progresses has been made

including Double Machine Learning (DML)

causal forests

and ML-assisted policy learning

those have integrated the predictive algorithms within orthogonalized identification strategies to improve nuisance estimation and flexible confounder adjustment.With the literature review and selected empirical applications

this study analyzed how ML tools operate at certain stages of causal identification. The analysis has specifically emphasized on the major advantages of applying ML in causal inference for the economic policy evaluation

such as the fact that it could perform better on the high-dimensional control selection and being able to target the subgroup more precisely

while also indicates the risks related to overfitting

inference validity

bias amplification

and the risks with relates to the causation. The study concludes that ML application could enhance the evaluation when they are applied within the causal frameworks that are supported by cross-fitting and effectiveness checks.

关键词

Keywords

references

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