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Interpreting evidence on development accelerators from non-randomised evaluations

This project is aimed at translating emerging evidence from non-randomised evaluations of development accelerators into concrete and actionable policy recommendations.  The Accelerate Hub has pioneered the use of quantitative statistical methods for identifying candidate development accelerators from non-randomised data. A next step is to guide prioritisation of identified candidates for further evaluation and incorporation into national policy. Two key considerations are necessary for this (1) Checking the causal assumptions underlying original analyses; (2) Understanding the cost-benefit trade off of different accelerator implementation scenarios. This project explores these two areas using the latest techniques in causal inference and machine learning, and innovative economic modelling approaches for data drawn from multiple sources.

Team:

William Rudgard (University of Oxford, United Kingdom)
Chris Desmond (University of KwaZulu-Natal, South Africa)
Sopuruchukwu Obiesie (University of Oxford, United Kingdom)
Marisa Casale (University of Western Cape, South Africa)
Robin Evans (University of Oxford, United Kingdom)
Mark Orkin (University of Oxford, United Kingdom)
Mia Granvik Saminathen (University of Cape Town)
Siyanai Zhou (University of Cape Town)

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