This grant funds work by a team led Francesca Dominici and Jose Zubizarreta to develop new methods and techniques that will increase the robustness and power of randomized controlled trials (RCTs) as a method for investigating causal relations across a diverse range of phenomena. Long-regarded as the gold standard in social scientific research, the randomized controlled trial has virtues in abundance. By randomly sorting participants into control and treatment groups, researchers using RCTs can, in theory, ensure that these groups are statistically indistinguishable. This allows them to conclude that differences later observed between these two groups must have been caused by the treatment. This works beautifully in principle. In practice, however, drawing causal inferences using RCTs can be bedeviled by a number of factors, all involving how statistical averages never tell the whole story. When the population under study is very diverse, for instance, randomly sorting participants into control and treatment groups may be insufficient to ensure the two groups are identical across all variables. In other instances, control and treatment groups may be insufficiently isolated from one another, allowing outcomes caused by the treatment to spillover into the control group. In other cases, the effect of a treatment within the treatment group may be unequally distributed. A treatment that benefits a few people greatly while leaving most people worse off, say, might appear to have a positive benefit on average, leading researchers to miss important facts about how that average benefit is generated. Dominici and her team will develop and test new statistical methods that, if successful, will help researchers design RCTs in ways that head off each of these problems and allow the design of RCTs that can be more reliably used to make causal inferences. Their results will be distributed through academic papers, talks at professional meetings, and through open-source software tools available to be downloaded by researchers.