Editing Causal Inference in AI (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Causation''' — A relationship where one event (the cause) brings about another event (the effect). Distinct from correlation.
* '''Correlation''' — A statistical association between two variables that does not imply causation ("correlation is not causation").
* '''Confounding variable''' — A hidden variable that influences both the apparent cause and effect, creating a spurious association.
* '''Intervention''' — Actively changing the value of a variable (rather than just observing it), denoted do(X=x) in do-calculus.
* '''Observational data''' — Data collected without intervening; correlations in observational data may not reflect causal relationships.
* '''Randomized Controlled Trial (RCT)''' — The gold standard for establishing causation: randomly assign units to treatment or control, then measure outcomes.
* '''Counterfactual''' — A hypothetical: "What would have happened if X had been different?" e.g., "Would this patient have survived if they had received the drug?"
* '''Potential outcomes framework''' — A formalization of causal inference using Y(1) (outcome if treated) and Y(0) (outcome if not treated) for each unit.
* '''Average Treatment Effect (ATE)''' — The average causal effect of a treatment across a population: E[Y(1) - Y(0)].
* '''DAG (Directed Acyclic Graph)''' — A graphical model where nodes are variables and directed edges represent causal relationships; no cycles.
* '''Backdoor criterion''' — A graphical criterion for identifying which variables to condition on to block spurious correlations (confounding paths) in a causal DAG.
* '''Do-calculus''' — A set of rules (developed by Judea Pearl) for computing the effect of interventions from observational data and a causal DAG.
* '''Instrumental variable''' — A variable that affects the treatment but has no direct effect on the outcome except through the treatment; used to estimate causal effects when confounding is present.
* '''Causal discovery''' — Algorithms for inferring causal structure (the DAG) from observational data.
* '''Selection bias''' — Bias arising when the sample used for analysis is not representative of the population of interest.
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