In the aftermath of a disaster, news and research attention is focused almost entirely on catastrophic narratives and the various drivers that may have led to the disaster. Learning from failure is essential to preventing future disasters. However, hyperfixation on the catastrophe obscures potential successes at the local scale, which could serve as important examples and learning resources in effective risk mitigation. To highlight effective risk mitigation actions that would otherwise remain unnoticed, we propose the use of probabilistic downward counterfactual analysis. This approach uses counterfactual modelling of a past hazard event with consequences made worse (i.e. downward counterfactual) by the absence of the mitigation intervention. The approach follows probabilistic risk analysis procedures where uncertainties in the simulated events and outcomes are accounted for and propagated. We demonstrate the method using a case study of Nepal’s School Earthquake Safety Program, implemented before the 2015 Mw 7.8 Gorkha earthquake. Using a school building database for Kathmandu Valley, Nepal, we present two applications: 1) the quantification of lives saved during the Gorkha earthquake as a result of the retrofitting of schools in Kathmandu Valley since 1997, 2) the quantification of the annual expected lives saved if the pilot retrofitting program was extended to all school buildings in Kathmandu Valley based on a probabilistic seismic hazard model. The shift in focus from realised outcome to counterfactual alternative enables the quantification of the benefits of risk reduction programs amidst disaster, or for a hazard that has yet to unfold. Such quantified counterfactual analysis can be used to celebrate successful risk reduction interventions, providing important positive reinforcement to decision-makers with political bravery to commit to the implementation of effective measures.

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This PhD journey would not have been possible without the support of countless people around the world. I have been privileged to work in the most supportive, fun, and intellectually vibrant environment. Here, I briefly describe those who have assisted me professionally and emotionally.To my adviser, David Lallemant, thank you for being a constant source of support and positive energy since day one of my PhD. My research process has been extremely rewarding because of the freedom, unwavering guidance, and enthusiasm that you provided. I learned from you how to become a more effective, reflective, and intentional researcher. I am confident to say that you have helped me build the most amazing PhD. You have my deep gratitude.I want to thank my esteemed thesis advisory committee members, Susanna Jenkins and Janice Teresa Ser Huay Lee, who have been a limitless source of wisdom and generosity over the last few years. Thank you to Susanna for the opportunity to collaborate with the volcano group in the Asian School of the environment, which lead to a tremendous learning experience. The learning curve for me have been steep, but you made sure I got the support I needed on the volcanology aspects, leading to one of the most rewarding research work I've done in the PhD -thank you. I am grateful to Janice for generously giving me advice on technical and non-technical aspects of being a researcher in the field. I learned how to write my first policy paper from your teaching, which made me realise new ways of thinking about success in risk reduction. Special thanks to Benoit Taisne who is generous with his time to join my thesis committee meetings while making sure he asks the tough (but super helpful) questions. Susanna, Janice, and Benoit, your patient, careful mentorship and engagement with my ideas has always made my commitment stronger and my work better. xi on the topic, and broaden my knowledge on the implications and capabilities of dynamic risk modelling. Thank you to