Structural causal model
A structural causal model represents causal relationships among variables through equations that determine each variable from other variables and exogenous factors.
Causal AI
A causal model is a statistical model built on assumptions about the causal process that generates data and supports reasoning about interventions.
- How it is used
- Models how causes generate effects so hypothetical interventions can support causal inference when experiments are infeasible.
- How deep it goes
- Defines variables and deterministic assignments, then works through a linear mechanism; later causal inferences are deferred.
- Examples
- Uses femur length and height to illustrate a linear assignment with a noise variable for other causes.
- What you can do after
- Build it from scratch by adding exogenous variables and structural assignments to a causal DAG.
Causal Inference for Data Science
A causal model is a conceptual representation of causal relationships that accounts for variables influencing both a cause and an effect.
- How it is used
- Frames intervention questions by representing causes, outcomes, and confounders before estimating causal effects.
- How deep it goes
- Progresses from intuitive DAGs to structural functions, endogenous variables, exogenous variables, and modular causal systems.
- Examples
- Uses discounts and customer loyalty, Pac-Man level design, medical treatment, product pricing, and three-node confounding graphs.
- What you can do after
- Evaluate/understand whether a proposed intervention model includes defensible confounders and assumptions rather than relying on predictive accuracy.
Read how Causal Inference for Data Science explains structural causal model →
Related concepts
- endogenous variablehas part · An endogenous variable is a variable in a structural causal model whose value is determined by the model’s relationships and exogenous inputs.
- exogenous variablehas part · An exogenous variable is a causal input whose variation originates outside the endogenous system represented by a structural causal model.
- assignment functionhas part · An assignment function is a deterministic rule in a structural causal model that determines an endogenous variable from its causal parents and exogenous inputs.
- causal graphical modelkind of · A causal graphical model is a probabilistic or structural model whose graph represents causal relationships and whose mechanisms support reasoning about interventions.
- directed acyclic graphuses · A directed acyclic graph is a graph with directed edges and no directed cycles.
- experimentationmeasured by · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
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