1. Statistical Methods
Statistical methods such as multivariable regression, stratification, and matching can help control for confounding variables. Sensitivity analyses can assess the robustness of the observed associations to potential biases.
2. Study Design
Well-designed studies, such as randomized controlled trials (RCTs) and cohort studies, can help minimize biases and confounding. RCTs, in particular, are considered the gold standard for establishing causal relationships due to the random allocation of participants, which helps control for confounding variables.
3. Hill's Criteria for Causation
Bradford Hill's criteria provide a useful framework for assessing causation, including factors such as strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy. While these criteria are not definitive, they can help assess whether an observed association is likely to be causal.
