GRADIENT-BOOSTED CAUSAL INFERENCE FRAMEWORK FOR POLICY RECOMMENDATION IN SMART GOVERNANCE SYSTEMS
DOI:
https://doi.org/10.29121/shodhkosh.v4.i2.2023.5543Keywords:
Causal Inference, X-Learner, Xgboost, Policy Recommendation, Smart Governance, Econml, Treatment Effect EstimationAbstract [English]
This study presents the Gradient-Boosted Causal Inference Framework that will contribute to effective data-driven policy decision making in the smart governance systems. The framework combines the X-Learner algorithm with XGBoost and allows you to accurately estimate individual treatment effects (ITE) in mixed and complex data scenarios. The model relying on the usage of the EconML library successfully integrated causal inference with advanced machine learning methods, improving the predictive power and explains the causal inference. Using simulated datasets of governance, the framework has proven major advances in estimation of policy values and treatment effect than conventional models would have. Using SHAP-based analysis also increases transparency giving policymakers the ability to view feature influence and decision pathways. The given proposed system is rather robust in incorporating imbalanced treatment groups and non-linear effects of policies, which can be considered to provide scalable solutions to governance facilitation in a number of domains. The findings point to the horizon that the approach can facilitate individualized, effective and evidence-based interventions in smart city settings and encourage more responsive and responsible societal decision making.
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