A modern, decision-theoretic framework can help clarify important practical questions of experimental design. Building on our recent work, this chapter begins by summarizing our framework for understanding the goals of experimenters, and applying this to re-randomization.We then use this...

A modern, decision-theoretic framework can help clarify important practical questions of experimental design. Building on our recent work, this chapter begins by summarizing our framework for understanding the goals of experimenters, and applying this to re-randomization. We then use this...

Formal analysis plans limit false discoveries by registering and multiplicity adjusting statistical tests. As each registered test reduces power on other tests, researchers prune hypotheses based on prior knowledge, often by combining related indicators into evenly-weighted indices. We propose...

How should researchers design panel data experiments? We analytically derive the variance of panel estimators, informing power calculations in panel data settings. We generalize Frison and Pocock (1992) to fully arbitrary error structures, thereby extending McKenzie (2012) to allow for...

Randomised controlled or clinical trials (RCTs) are generally viewed as the most reliable method to draw causal inference as to the effects of a treatment, as they should guarantee that the individuals being compared differ only in terms of their exposure to the treatment of interest. This...