Causal inference methods to study nonrandomized, preexisting development interventions.


Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children <5 y old and measured outcomes over 12 mo. Although we found a 33 percentage point difference in new toilet construction [95% confidence interval (CI) = 28%, 39%], we found no impacts on height-for-age Z scores (adjusted difference = 0.01, 95% CI = -0.15, 0.19) or diarrhea (adjusted longitudinal prevalence difference = 0.003, 95% CI = -0.001, 0.008) among children <5 y old. This study demonstrates that matched cohort designs can estimate impacts from nonrandomized, preexisting interventions that are used widely in development efforts. Interpreting the impacts as causal, however, requires stronger assumptions than prospective, randomized studies.

DOI: 10.1073/pnas.1008944107

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@article{Arnold2010CausalIM, title={Causal inference methods to study nonrandomized, preexisting development interventions.}, author={Benjamin F Arnold and Ranjiv S . Khush and Padmavathi Ramaswamy and Alicia G London and Paramasivan Rajkumar and Prabhakar Ramaprabha and Natesan Durairaj and Alan Hubbard and Kalpana P Balakrishnan and John M . Colford}, journal={Proceedings of the National Academy of Sciences of the United States of America}, year={2010}, volume={107 52}, pages={22605-10} }