Liquidity Constraints and Household Financial Vulnerability in China: An Explainable, Uncertainty-Aware Machine Learning Risk Management Framework Using the China Household Finance Survey

Household financial vulnerability reflects the likelihood that a household will fall into financial distress when facing adverse shocks, and it is a key micro-foundation of financial system stability. Using microdata from the China Household Finance Survey (CHFS), this study develops a machine-learning-based risk management framework to examine how liquidity constraints affect household financial vulnerability and to identify high-risk groups under heterogeneous socioeconomic conditions. Household financial vulnerability is operationalized as a binary outcome, denoted as the Financial Vulnerability Index (FVI), indicating whether liquid buffers are sufficient to cover unexpected expenditures. Liquidity constraints are measured through credit accessibility indicators, forming a binary Liquidity Constraint (LC) variable. The framework integrates (i) high-performance tabular prediction models, including gradient-boosted decision trees and neural tabular networks, to construct calibrated probability-of-vulnerability scores; (ii) explainability techniques, with Shapley Additive Explanations (SHAP) used to quantify global and local risk drivers; and (iii) causal machine learning methods, such as Double Machine Learning (DML) and generalized random forests, to estimate the heterogeneous causal effect of liquidity constraints on financial vulnerability across income groups, city tiers, and regions. To enhance model reliability for risk governance, probability calibration and distribution-free uncertainty quantification are implemented via conformal prediction. Empirical results indicate that liquidity constraints significantly increase the predicted and causally estimated risk of household financial vulnerability, with stronger effects concentrated among middle-to-lower income households and households located in lower-tier cities and economically stressed regions. The proposed framework provides an algorithmic basis for targeted inclusive-finance interventions and household risk mitigation policies.