===== Causal Inference =====

'''Causal inference''' is the process of determining whether a specific factor (exposure, variable, or intervention) directly causes a change in an outcome, rather than merely being associated with it.

==== Key distinction ====
  * **Association** ≠ **Causation**  
    A variable may correlate with an outcome without causing it.

==== Methods for causal inference ====
  * **Randomized controlled trials (RCTs)** – gold standard for establishing causality.
  * **Quasi-experimental designs** – such as difference-in-differences, instrumental variables.
  * **Causal modeling** – e.g., directed acyclic graphs (DAGs), structural equation models.
  * **Counterfactual reasoning** – comparing observed outcomes with hypothetical alternatives.

==== In observational studies ====
Causal inference requires:
  * Control of confounders
  * Attention to bias (selection, measurement, etc.)
  * Proper temporal sequence (cause precedes effect)

==== Clinical importance ====
Causal inference underpins:
  * Treatment effect estimation
  * Policy decisions
  * Guideline development

'''In summary:''' causal inference seeks to answer the question: “Does X cause Y?”, not just “Is X related to Y?”