Benjamin F. Arnold|Ranjiv Khush|R. Padmavathi|Alicia G. London|Paramasivan Rajkumar|Prabhakar Ramaprabha|Durairaj Natesan|Alan Hubbard|Kalpana Balakrishnan|John M. Colford
Causal inference methods to study nonrandomized, preexisting development interventions
University of California, Berkeley|Retina Institute|Sri Ramachandra Institute of Higher Education and Research|Retina Institute|Sri Ramachandra Institute of Higher Education and Research|Sri Ramachandra Institute of Higher Education and Research|Sri Ramachandra Institute of Higher Education and Research|University of California, Berkeley|Sri Ramachandra Institute of Higher Education and Research|University of California, Berkeley
empirical measurement interventions address significant global health development problems necessary ensure resources applied appropriately intervention programs often deployed group community level gold standard design measure effectiveness communitylevel interventions communityrandomized trial conditions trials often make difficult assess external validity sustainability sheer number community interventions relative randomized studies speaks need rigorous observational methods measure impact article use potential outcomes model causal inference motivate matched cohort design study impact sustainability nonrandomized preexisting interventions illustrate method using sanitation mobilization water supply hygiene intervention rural india matched sample 25 villages enrolled 1284 children 5 old measured outcomes 12 mo although found 33 percentage point difference new toilet construction 95 confidence interval ci 28 39 found impacts heightforage z scores adjusted difference 001 95 ci 015 019 diarrhea adjusted longitudinal prevalence difference 0003 95 ci 0001 0008 among children 5 old study demonstrates matched cohort designs estimate impacts nonrandomized preexisting interventions used widely development efforts interpreting impacts causal however requires stronger assumptions prospective randomized studies