[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120605-en":3,"doc-seo-120605-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120605,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","CLARE - A Causal machine Learning Approach to Resilience Estimation","This paper proposes CLARE (Causal machine Learning Approach to Resilience Estimation), a resilience index grounded in impact evaluation frameworks and causal machine learning applied to longitudinal household survey data. The indicator is model-agnostic, data-driven, scalable, and anchored to wellbeing thresholds, enabling both shock-specific and general-purpose resilience measurement. Using over 28,000 observations from 19 nationally representative multi-topic surveys in Malawi, Nigeria, Tanzania, and Uganda (2009–20), the paper empirically constructs CLARE for drought resilience while showing applicability to other shocks. Results indicate CLARE outperforms existing resilience metrics and alternative methods for predicting food insecurity in dynamic forecasting and cross-sectional settings, and it can decompose the relative importance of resilience capacities for insulating populations from shocks. The index supports designing, targeting, and monitoring policies and investments, improving early-warning systems and resilience interventions and transferring guidance from data-rich to data-poor environments.","Public Disclosure Authorized Public Disclosure Authorized  \n\n| Policy Research Working Paper 11292\u003Cbr>CLARE\u003Cbr>A Causal machine Learning Approach to Resilience Estimation\u003Cbr>Talip Kilic\u003Cbr>Marco Letta\u003Cbr>Pierluigi Montalbano\u003Cbr>Federica Petruccelli |  |  |  |\n| --- | --- | --- | --- |\n| \u003Cbr>Development Economics Development Data Group January 2026\u003Cbr> |  |  |  |\n|  |  |  | A verified reproducibility package for this paper is available at [http://reproducibility.worldbank.org](http://reproducibility.worldbank.org) , click here for direct access. |\n\nPolicy Research Working Paper 11292  \nAbstract  \nThis 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 ora general-purpose resilience metric. The paper providesan 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  \nthat CLARE outperforms existing resilience metrics and alternative approaches to predict food insecurity out-ofsample—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.  \nThis paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at [http://www.worldbank.org/prwp. The authors may](http://www.worldbank.org/prwp. The authors may)[be contacted at tkilic@worldbank.org and marco.letta@uniroma1.it. A verified reproducibility package for this paper is](be contacted at tkilic@worldbank.org and marco.letta@uniroma1.it. A verified reproducibility package for this paper is)[ ](be contacted at tkilic@worldbank.org and marco.letta@uniroma1.it. A verified reproducibility package for this paper is)available at [http://reproducibility.worldbank.org](http://reproducibility.worldbank.org), click here for direct access.  \nThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they rep","cbCaiaS8bX1jEeQ1","https://ap.wps.com/l/cbCaiaS8bX1jEeQ1","pdf",1106629,1,66,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is CLARE and what methodology does it use?\",\"answer\":\"CLARE is a resilience index built from an impact evaluation framework and causal machine learning applied to longitudinal household survey data. It is designed to be model-agnostic and data-driven.\"},{\"question\":\"How is CLARE validated and what data is used in the empirical demonstration?\",\"answer\":\"The paper demonstrates constructing CLARE using more than 28,000 household observations from 19 nationally representative longitudinal surveys conducted in Malawi, Nigeria, Tanzania, and Uganda during 2009–20. The analysis focuses on drought resilience but targets broader shock relevance.\"},{\"question\":\"How does CLARE perform compared with existing resilience metrics?\",\"answer\":\"CLARE outperforms existing resilience metrics and alternative approaches for predicting food insecurity out of sample. It shows stronger results in both future-oriented dynamic forecasting and held-out country cross-sectional prediction.\"}]","CLARE - A Causal machine Learning Approach to Resilience Estimation | PDF",1785730857,166,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"clare-a-causal-machine-learning-approach-to-resilience-estimation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/clare-a-causal-machine-learning-approach-to-resilience-estimation/120605/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is CLARE and what methodology does it use?","Question",{"text":75,"@type":76},"CLARE is a resilience index built from an impact evaluation framework and causal machine learning applied to longitudinal household survey data. It is designed to be model-agnostic and data-driven.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is CLARE validated and what data is used in the empirical demonstration?",{"text":80,"@type":76},"The paper demonstrates constructing CLARE using more than 28,000 household observations from 19 nationally representative longitudinal surveys conducted in Malawi, Nigeria, Tanzania, and Uganda during 2009–20. The analysis focuses on drought resilience but targets broader shock relevance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does CLARE perform compared with existing resilience metrics?",{"text":84,"@type":76},"CLARE outperforms existing resilience metrics and alternative approaches for predicting food insecurity out of sample. It shows stronger results in both future-oriented dynamic forecasting and held-out country cross-sectional prediction.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]