Conceptualizing experimental controls using the potential outcomes framework

TL;DR

This paper introduces a precise, unified framework for defining and using experimental controls based on potential outcomes, enhancing the design and interpretation of experiments across sciences.

Contribution

It provides a clear mathematical definition of experimental controls within the potential outcomes framework, unifying concepts across biological and social sciences.

Findings

Defines experimental controls using potential outcomes

Shows how controls can diagnose design flaws

Highlights underutilization of controls in research

Abstract

The goal of a well-controlled study is to remove unwanted variation when estimating the causal effect of the intervention of interest. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative" and "positive" controls, which are measurements designed to detect systematic sources of unwanted variation. Here, we introduce clear, mathematically precise definitions of experimental controls using potential outcomes. Our definitions provide a unifying statistical framework for fundamental concepts of experimental design from the biological and other basic sciences. These controls are defined in terms of whether assumptions are being made about a specific treatment level, outcome, or contrast between outcomes. We discuss experimental controls as tools for researchers to wield in designing experiments and detecting potential design flaws, including using controls to diagnose unintended factors that influence the outcome of interest, assess measurement error, and identify important subpopulations. We believe that experimental controls are powerful tools for reproducible research that are possibly underutilized by statisticians, epidemiologists, and social science researchers.