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Type of the article: Research ArticleAbstractDigital financial inclusion (DFI) has become a critical driver of sustainable growth and financial resilience in the digital era, yet its implications for bank stability remain ambiguous, particularly across heterogeneous institutional contexts. This study examines whether and under what conditions DFI fosters bank stability, using data from 65 emerging and advanced economies during 2010–2022. Employing Double Machine Learning (DML) and Causal Forests to address endogeneity and treatment heterogeneity, together with Panel Threshold Regression (PTR) to capture nonlinear dynamics, the paper provides a causal and structural assessment of the DFI–stability nexus. Results reveal that, on average, DFI exerts no statistically significant impact on bank stability across the full sample. However, substantial heterogeneity emerges in financially developed and institutionally strong economies, DFI significantly enhances stability (CATE = +0.0165, p lt; 0.001), while in underdeveloped systems it weakens it (CATE = –0.0082, p lt; 0.001). The PTR model identifies a critical DFI threshold (–1.3611), below which DFI undermines stability and above which its effect becomes neutral, confirming nonlinear regime behavior. These findings highlight that DFI alone cannot guarantee stability; its benefits materialize only within robust institutional and financial ecosystems. Methodologically, the integration of causal machine learning and threshold modeling offers a novel framework for examining digital finance policies and contributes to a deeper understanding of conditional digital effectiveness in modern banking systems.
Phan et al. (Wed,) studied this question.