Conceptualizing Experimental Controls Using the Potential Outcomes Framework

The goal of a controlled experiment is to remove unwanted variation when estimating the causal effect of the intervention. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative" and "positive" controls, which are additional experimental components designed to detect systematic sources of variation. We introduce a taxonomy of clear, mathematically-precise definitions of experimental controls using the potential outcomes framework. These definitions are intended for pedagogical purposes: they may be used by educators to ensure that their students adhere to good statistical practice, and may also be useful for communication with practitioners who are less familiar with statistical concepts. We define three types of experimental controls based on assumptions about potential outcomes: treatment, outcome, and contrast controls. After each type of control is introduced, we provide examples of its use. We also discuss experimental controls as tools for researchers to use in designing experiments and detecting potential design flaws, such as identifying unwanted variation. We believe that experimental controls are powerful yet underutilized tools for reproducible, replicable, rigorous, and transparent research.