Can Inclusive Finance Policies Alleviate Household Financial Vulnerability: Causal Inference based on a Double Machine Learning Model

Yong Xing

doi:10.6981/FEM.202511_6(11).0009

Authors

Yong Xing

DOI:

https://doi.org/10.6981/FEM.202511_6(11).0009

Keywords:

Inclusive Finance Policies; Household Net Assets; Household Financial Vulnerability; Dual Machine Learning; Causal Inference.

Abstract

Household financial vulnerability exposes families to dual constraints at the micro level: debt repayment crises and consumption contraction. To address the household financial vulnerability dilemma in China, this paper develops a dual machine learning framework that incorporates both predictive and causal inference models. Using data from the China Family Panel Studies (CFPS), the paper employs causal inference through the dual machine learning model to explore the mechanisms through which inclusive finance policies affect household financial vulnerability in China. The study finds that inclusive finance policies alleviate financial constraints and reduce household financial vulnerability by optimizing household financial asset allocation. An increase in household net assets effectively promotes the optimization of household financial asset allocation through inclusive finance policies, thus playing a positive moderating role. This study provides theoretical and empirical insights for addressing the challenges of household financial vulnerability.

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