Estimating subgroup impacts in an RCT can be hard. An RCT by itself is usually underpowered for this task–we barely have enough data to give us an overall average, and as subgroups are smaller, they are noisier! One idea recently gaining increased traction is to augment an RCT with observational data. We might use …

Do you ever want to visualize the distribution of effects across sites in a multi-site evaluation (or meta analysis)? For example, consider a multisite trial with 30 sites, where each site is effectively a small randomized experiment. A researcher might fit a multilevel model with a random effect for the impact in each …

There are a lot of ways to do power calculations, but I wanted to show off a package, PUMP, that my collaborators and I recently set out into the world. The original purpose of the package was to provide tools for power analyses that account for multiple outcomes under a variety of common experimental designs—an …

When does blocking help? Should we always do it if we can? In this post we talk about how there are actually different kinds of blocking, and different ways of thinking about where blocks come from. The question of whether blocking can ever cause harm turns out to depend on these perspectives.

When estimating average treatment impact for a multisite randomized trial, recent literature (see Bloom et al. (2017)) has suggested using the so-called FIRC (Fixed Intercept, Random Coefficient) model. There are some technical details that make fitting this model slightly tricky in R; this post talks through these …