We provide twelve best practices and discuss how each practice can help researchers accurately, credibly, and ethically use Generative AI (GenAI) to enhance experimental research. We split the twelve practices into four areas. First, in the pre-treatment stage, we discuss how GenAI can aid in...

This study investigates how to use regression adjustment to reduce variance in experimental data. We show that the estimators recommended in the literature satisfy an orthogonality property with respect to the parameters of the adjustment. This observation greatly simplifies the derivation of...

Eligibility criteria for interventions can induce an Ashenfelter Dip, and subsequent mean-reversion may result in improvement over time even absent the intervention. We investigate these dynamics for a food-as-medicine program to treat diabetes, where eligibility required elevated hemoglobin A1c...

Randomized control trials (RCTs) have become increasingly important as an evidence-based method to evaluate interventions such as government programs and policy initiatives. Frequently, however, RCTs are characterized by "imperfect compliance," in that not all the subjects who are randomly...

In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse-selection models. Despite significant data limitations, we are able to derive informative bounds on demand...

This paper describes a number of strategies that experimenters may use to improve the external validity of their own findings, and of their research field as a whole. The paper emphasizes a dynamic view of research processes, in which learning about treatment and treatment adoption does not...