TY  - RPRT
        AU  - Budría, Santiago
AU  - Fermé, Eduardo
AU  - Freitas, Diogo Nuno
        TI  - Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention
        PY  - 2025/Feb/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 17669
        UR  - https://www.iza.org/publications/dp17669
        AB  - Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined with SHAP (SHapley Additive exPlanations) values to document the short- and long-term effects of individual and contextual factors—such as income, energy prices, and regional conditions—on future energy poverty outcomes. The findings emphasize the importance of policies focused on income stability and may be used to shift the policy focus from reactive measures, which address existing poverty, to preventive strategies that target households showing early signs of vulnerability.
        KW  - Energy poverty
KW  - panel data
KW  - explainable AI
KW  - time-series analysis
KW  - public policy
KW  - temporal dynamics
KW  - feature importance
        ER  -