Causal inference

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Causal inference refers to the process of determining whether there is a cause-and-effect relationship between two or more variables or events (Hammerton & Munafò, 2021). It involves drawing conclusions about the causal effects based on observed associations or data. Causal inference is concerned with understanding how changes in one variable affect another variable and identifying the mechanisms or factors that contribute to the observed relationship. In their article on causal inference, Costa and Yakusheva (2016) explained the significance of causal inference in nursing research and explored study designs that have a higher likelihood of generating causal findings. They provided an overview of the conceptual framework that underlies this discussion and subsequently utilized specific examples from existing literature to exemplify three primary study designs: cross-sectional studies, longitudinal studies, and randomized controlled trials (RCTs). Through their discussion, the authors highlighted the strengths and limitations of the available evidence, with a particular focus on understanding the causal relationship between nurse staffing and outcomes.

The focus of my quality improvement project is using text messaging to impact diabetes outcomes. Causal inference in the context of this QI involves determining whether a cause-and-effect relationship exists between the text messaging intervention and improved diabetes management outcomes. The process requires evaluating whether the intervention is responsible for the observed changes and understanding how it affects the outcomes. To imply causal inference in this project, several key steps should be followed. First, the intervention needs to be clearly defined, including its purpose, content, frequency, and target audience. Consistency in implementing the intervention across the project is crucial. Next, specific outcomes of interest need to be identified. These could include glycemic control, medication adherence, self-monitoring of blood glucose, or lifestyle modifications. It is important to determine which outcomes the text messaging intervention is expected to influence in diabetes management. Random allocation of participants to either the intervention or control group helps minimize selection bias and ensures that any observed differences in outcomes can be attributed to the intervention rather than other factors (Hammerton & Munafò, 2021). Baseline data should be collected before the intervention begins. The baseline data in this case will be patients’ HbA1c levels. Establishing this starting point for comparison helps identify potential confounding variables that need to be monitored. The text messaging intervention should then be implemented according to a predefined protocol. Consistency in delivering the intervention is crucial, while ensuring that participants in the control group do not receive any similar interventions. Throughout the intervention period, data on the identified outcomes will be continuously collected and the collected data will be subjected to statistical analysis to compare the outcomes. Finally, based on the analysis results, it will be possible to draw causal inferences regarding the relationship between the text messaging intervention and the outcomes of interest.