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Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables

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  • Shunxin Yao

Abstract

This study utilizes a simulated dataset to establish Python code for Double Machine Learning (DML) using Anaconda's Jupyter Notebook and the DML software package from GitHub. The research focuses on causal inference experiments for both binary and continuous treatment variables. The findings reveal that the DML model demonstrates relatively stable performance in calculating the Average Treatment Effect (ATE) and its robustness metrics. However, the study also highlights that the computation of Conditional Average Treatment Effect (CATE) remains a significant challenge for future DML modeling, particularly in the context of continuous treatment variables. This underscores the need for further research and development in this area to enhance the model's applicability and accuracy.

Suggested Citation

  • Shunxin Yao, 2025. "Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables," Papers 2502.19898, arXiv.org.
    • Handle: RePEc:arx:papers:2502.19898
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    References listed on IDEAS

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    1. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
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