A TWO-GATE SHAP–CAUSAL MACHINE LEARNING FRAMEWORK FOR IDENTIFYING ACTIONABLE POLICY LEVERS IN EDUCATIONAL INEQUALITY

Registration ID: IJNRD_327962

Published ID: IJNRD2608266

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Anmol Adhikari, Gagan Tiwari

Explainable AI, SHAP, causal machine learning, Double Machine Learning, causal forest, regression discontinuity, education policy, Nepal, educational inequality, treatment effect estimation.

Governments increasingly use machine learning to rank the “drivers” of social outcomes, but feature importance is a statement about prediction, not causation a distinction routinely collapsed in applied policy analytics. This paper proposes and validates a two-stage hybrid framework that first identifies candidate drivers of district-level literacy in Nepal using gradient-boosted trees (XGBoost) with SHAP (SHapley Additive exPlanations) attribution, then subjects the top candidates to causal validation using Double Machine Learning (Double ML) and Causal Forests, with Propensity Score Matching (PSM), Difference-in-Differences (DiD) and Regression Discontinuity Design (RDD) as independent triangulation. The framework is demonstrated on a district-level dataset (N = 77) built by anchoring official province-level statistics from six national sources to a reproducible district-resolution panel. The SHAP stage ranks Teacher Quality, Sanitation (WASH), Water Access and Digital Access as the highest-magnitude predictors of literacy. Causal validation confirms a statistically significant average treatment effect only for Teacher Quality (θ = +0.181, p < 0.01) and Digital Access (θ = +0.104, p < 0.05); Infrastructure, despite plausible priors, shows a positive but statistically indistinguishable-from-zero effect (θ = +0.075, 95% CI crosses 0). Quasi-experimental triangulation via DiD and RDD around existing scholarship and poverty-eligibility policies detects no significant discontinuity. We present this as a validation of a transferable, dataset-agnostic methodology not as a finished national policy prescription and argue it generalizes as a low-cost safeguard against misallocating public budgets on the basis of correlational ML alone.

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Anmol Adhikari & Gagan Tiwari (August-2026). A TWO-GATE SHAP–CAUSAL MACHINE LEARNING FRAMEWORK FOR IDENTIFYING ACTIONABLE POLICY LEVERS IN EDUCATIONAL INEQUALITY. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(8), c670-c675. https://ijnrd.org/papers/IJNRD2608266.pdf

Volume 11 Issue 8, August-2026

Pages : c670-c675

Paper Reg. ID: IJNRD_327962

Published Paper Id: IJNRD2608266

Research Area: Other area not in list

Author Type: Indian Author

Country: Noida, UP, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2608266.pdf

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