Observational Studies: Methods to Improve Causal Inferences Bernadette Capili, PhD, NP-C [Director] Heilbrun Family Center for Research Nursing, The Rockefeller University, 1230 York Avenue, Hospital, Room 106, New York, NY 10065 Previous articles in this series discussed observational study designs, including cross- sectional, cohort, and case-control studies. This paper focuses on understanding causal inferences and methods to improve them for observational studies. For example, an observational study might show an association (correlation) between a predictor variable (independent variable) and an outcome (dependent variable); however, the results may not represent cause and effect (Hulley, 2013). Causal inference implies an intervention, e.g., treatment or behavior was the ‘cause’ of the effect (or outcome). Understanding causal inferences between predictor(s) and outcome(s) can provide insights to understanding the etiology of a disease, identify methods to prevent or reduce disease (or occurrence) and potentially initiate the development of treatments (Hulley, 2013). For example, are eating carrots associated with improved eye health? It is important to note that some associations found in an observational study do not represent cause and effect. However, there are well-recognized explanations for associations between a predictor and outcome in such studies (Hulley, 2013). These occur by chance (random error), bias (systematic error), and confounding variables. The remainder of this paper will describe each item and methods to minimize these factors. Explanations: Chance/Random Error Chance or random error creates associations between a predictor and an outcome from an observational study sample which is erroneous (Hulley, 2013). In other words, the association does not exist in the population. For example, let’s say that there is no association between sedentary behavior and obesity among HIV+ participants seeking care from a specific clinic, wherein 35% are sedentary based on a physical activity questionnaire. If 20 HIV+ participants who are obese (body mass index (BMI) 30 or greater) and 20 HIV+ participants who are normal weight (BMI less than 24.9) were selected for a study, it would be expected that 7 participants per group (35% of 20) would be sedentary, using a physical activity questionnaire. However, by chance, 12 sedentary participants are in the obese group and 4 in the normal weight group. Thus, the observed outcome is an erroneous (spurious) association between physical activity and obesity if this occurs. Chance is also called random error because it does not have an explanation. However, if the association from a random error is statistically (212) 327-8405, bcapili@rockefeller.edu. HHS Public Access Author manuscript Am J Nurs. Author manuscript; available in PMC 2023 March 23. Published in final edited form as: Am J Nurs. 2023 January 01; 123(1): 45–49. doi:10.1097/01.NAJ.0000911536.51764.47. Author Manuscript Author Manuscript Author Manuscript Author Manuscript