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spillover effects

Power Calculation Software for Randomized Saturation Experiments

Berk Ozler's picture

One of the things I get asked when people are designing experiments – when they are either interested in or worried about spillover effects – is how to divvy up the clusters into treatment and control and what share of individuals within treatment clusters to assign within-cluster controls. The answer seems straightforward – it may look intuitive to assign a third to each group and I have seen a few designs that have done this, but it turns out that it’s a bit more complicated than that. There was no software that I am aware of that helped you with such power calculations, until now...

Definitions in RCTs with interference

Berk Ozler's picture

On May 25, I attended a workshop organized by the Harvard School of Public Health, titled “Causal Inference with Highly Dependent Data in Communicable Diseases Research.” I got to meet many of the “who’s who” of this literature from the fields of biostatistics, public health, and political science, among whom was Elizabeth Halloran, who co-authored this paper with Michael Hudgens – one of the more influential papers in the field.

Designing experiments to measure spillover effects

Berk Ozler's picture

Many programs affect those who were not directly targeted by the intervention. We know this for medical interventions (e.g. deworming: Kremer and Miguel 2004); cash transfer programs (e.g. PROGRESA: Angelucci and de Giorgi 2009); and now voter awareness programs (Giné and Mansuri 2011).