Correlation vs Causation

No. A very strong correlation is necessary but not sufficient for causation. Example: the number of firefighters at a fire is strongly correlated with fire damage (r might be 0.8+), but firefighters do not cause damage—they respond to large fires, which cause damage. Confounding by fire size explains the correlation. Always investigate alternative explanations before claiming causation.

Confounding occurs when a third variable (confounder) is associated with both the cause and effect, creating a spurious association. Example: age confounds the relation between shoe size (cause) and reading ability (effect) because age is associated with both. To identify confounders: (1) Think logically about what affects both variables. (2) Check if the association weakens after adjusting for the confounder statistically. (3) Use causal diagrams (DAGs) to visualize the causal structure.

For establishing causation, yes—RCTs are the gold standard. But RCTs have limitations: they are expensive, may be unethical, and may not reflect real-world conditions (external validity). Observational studies are necessary for studying long-term effects, rare outcomes, and exposures that cannot be randomly assigned. Use RCTs for causal hypothesis testing; use well-designed observational studies for hypothesis generation and real-world effectiveness.

Reverse causation occurs when you incorrectly infer the direction of causality. Example: does depression cause insomnia or does insomnia cause depression? Avoid reverse causation by: (1) Establishing temporal precedence with longitudinal designs (measure cause before outcome). (2) Using theory and prior research to guide causal direction. (3) Considering bidirectional relationships; some causal pathways are mutually reinforcing. Cross-sectional data cannot establish direction; use prospective cohort studies.

Unmeasured confounders are variables that confound the association but were not measured in the study. You cannot statistically control for variables you did not measure. Address unmeasured confounding by: (1) Measuring and adjusting for likely confounders in the study design. (2) Sensitivity analyses: assume different amounts of unmeasured confounding and see if conclusions change. (3) Using instrumental variables or natural experiments, which can estimate causal effects even with unmeasured confounding. (4) Discussing limitations honestly in the paper.