Causal inference in healthcare: effective evaluation of clinical programs and other applications

The pursuit of causal understanding in healthcare is a centuries-old endeavor, essential for professionals and researchers dedicated to determining the effects of interventions on patient health. This pursuit has progressed with the development of methodologies in causal inference, pivotal for steering clinical practices and health policy decisions. The evolution of this field is notably shaped by two dominant methodologies: clinical trials and observational studies. Clinical trials: Rooted in history with James Lind's seminal scurvy trial, clinical trials have become fundamental in establishing the efficacy and safety of medical interventions. These controlled, randomized experiments are crucial in determining the causal impact of interventions, thus setting the benchmark for causality in healthcare. Observational studies: Observational studies, in contrast, assess interventions' effects in naturalistic settings, drawing on data sources like electronic health records and administrative databases. While invaluable for exploring outcomes not readily tested in trials, these studies grapple with confounding and selection biases, presenting challenges in confirming causality. These biases - confounding, where third variables influence both exposure and outcome, and selection bias, where the study population does not represent the broader group - are significant hurdles in observational research. As the healthcare landscape becomes increasingly complex with a surge in real-world data and personalized medicine, the demand for rigorous causal inference methods intensifies. This chapter's main aim is to demystify causal inference methods for observational healthcare studies, offering insights into the methodologies that have been widely applied. This discussion intends to furnish readers with the necessary knowledge and tools to critically evaluate and utilize these methods within their healthcare domains.