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Causal Inference
A set of statistical methods for estimating causal effects from observational or experimental data.
Directed Acyclic Graphs (DAGs) Causal Graphs
Mathematical representation of cause-and-effect relationships in the form of directed acyclic graphs.
Structural Causal Models
Formal framework combining structural equations and graphs to model causal mechanisms.
Pearl's Causal Calculus
Rule system (do-calculus) enabling the derivation of causal effects from observed correlations.
Counterfactual Analysis
Study of hypothetical scenarios and what would have happened if different actions had been taken.
Discovery of Causality
Algorithms for automatically identifying causal structures from raw data.
Instrumental Variables
Technique using external variables to identify causal effects in the presence of confounding.
Causal Mediation
Analysis of the intermediate mechanisms through which a cause produces its effect.
Confounding Bias
Study and correction of factors that influence both the cause and the effect, creating spurious associations.
Propensity Scores
Method for estimating the probability of treatment to correct for selection bias in observational studies.
Temporal Causality
Analysis of cause-effect relationships in time series and longitudinal data.
Causal Inference in High Dimension
Methods adapted to data with many variables and limited samples.
Causal Reinforcement Learning
Integration of causal principles into RL algorithms to improve generalization.
Granger Causality Tests
Statistical tests to determine if one time series predicts another time series.
Structural Equation Models
Statistical approach combining factor analysis and regression to model complex causal relationships.