What Challenges Exist in Making Causal Inferences?
Several challenges complicate causal inference in epidemiology, including confounding variables, bias, and measurement error. Confounding occurs when an extraneous factor is associated with both the exposure and the outcome, potentially misleading conclusions. Bias can arise from systematic errors in study design or data collection, while measurement error involves inaccuracies in measuring exposures or outcomes.
