Regression Discontinuity Designs (RDDs) are a type of quasi-experimental research design that can be used to estimate the causal effect of a treatment or policy on a particular outcome. The design relies on the idea that the assignment of treatment or access to a program is based on a continuous variable, such as a score on a test or an age threshold. The RDD is used to estimate the causal effect of the program or treatment by comparing outcomes for individuals just above and just below the threshold.

RDDs have several advantages over other quasi-experimental designs, such as randomized controlled trials or propensity score matching. One of the main benefits is that RDDs can be used in situations where randomization is not possible or practical. Additionally, RDDs can control for potential confounding factors that may be associated with the threshold, such as socioeconomic status or pre-treatment outcomes. However, RDDs also have some limitations, such as the potential for measurement error and the need for a large sample size to detect small treatment effects.

RDDs are an important tool in the impact evaluation toolbox and are widely used in fields such as economics, political science and education to estimate causal effect of policies and programs. Due to their versatility and the ability to control for potential confounders, RDDs have become increasingly popular in recent years as a way to estimate causal effects in settings where randomization is not possible.