CLARE : A Causal machine Learning Approach to Resilience Estimation

This paper proposes a new resilience index, CLARE (Causal machine Learning Approach to Resilience Estimation), which is rooted in an impact evaluation framework and causal machine learning algorithms applied to longitudinal household survey data. The indicator is model-agnostic, data-driven, scalable, and normatively anchored to wellbeing thresholds, and can be either shock-specific or a general-purpose resilience metric. The paper provides an empirical demonstration of constructing the CLARE resilience index, leveraging more than 28,000 household observations from 19 nationally representative, longitudinal, multi-topic surveys that were implemented by the national statistical offices in Malawi, Nigeria, Tanzania, and Uganda over 2009–20 in partnership with the World Bank Living Standards Measurement Study. Although the paper centers on measuring resilience to drought, the proposed index is applicable to any type of shock. The analysis shows that CLARE outperforms existing resilience metrics and alternative approaches to predict food insecurity out-of-sample—both in the future (dynamic forecasting) and in held-out countries (cross-sectional prediction). The index can be decomposed to causally identify the relative importance of resilience capacities that can insulate populations from shocks. Thus, it can be operationalized in designing, targeting, and monitoring policies and investments that aim to strengthen resilience. CLARE’s deployment—paired with continued investments in national longitudinal survey platforms—can boost the effectiveness of early-warning systems and resilience-building interventions, while allowing the transfer of resilience policy advice from data-rich contexts to data-poor environments that may not immediately provide the requisite longitudinal survey data for index estimation.