AI-Driven Causal Inference for Evaluating Microfinance Impacts on Poverty Alleviation: Advanced Methods and Empirical Insights

ABSTRACT   This study applies AI-enhanced causal inference methods to rigorously assess microfinance interventions' effects on multidimensional poverty metrics in low-resource settings. Utilizing double machine learning (DML) frameworks combined with counterfactual simulation techniques, we analyze longitudinal data from rural Bangladesh microfinance programs (N=4,850 households, 2018-2023). Our hybrid computational-empirical approach addresses key limitations of traditional randomized controlled trials, including scalability constraints and unobserved confounding. Results demonstrate that AI-augmented causal models estimate an average treatment effect (ATE) of 18.3% income increase among microloan recipients, with pronounced heterogeneity: women-led households exhibit 24.7% gains versus 12.1% for male-led counterparts. Conditional average treatment effects (CATE) reveal targeting inefficiencies, with 32% of high-impact households under-served. Robustness checks using sensitivity analyses confirm minimal selection bias (Rosenbaum bounds Γ=1.4). These findings advocate for integrating adaptive AI tools in development policy evaluations, enabling real-time refinement of microfinance targeting strategies and enhancing poverty alleviation efficacy in resource-constrained environments. Keywords: AI-Enhanced Causal Inference, Double Machine Learning (DML), Microfinance Impact Evaluation, Counterfactual Simulation, Multidimensional Poverty Analysis