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Endogenous variable

An endogenous variable is a variable in a structural causal model whose value is determined by the model’s relationships and exogenous inputs.

How 2 Manning books explain it

Causal AI

An endogenous variable is a variable in a structural causal model whose value is determined by an assignment function from its causes and exogenous inputs.

How it is used
Identifies the explicitly modeled variables whose values an SCM computes and whose hypothetical outcomes counterfactual inference predicts.
How deep it goes
Defines deterministic assignments, contrasts their randomness with conditional sampling, and notes that data alone cannot identify the ground-truth SCM.
Examples
Uses a Python rock-throwing process and an additive enzyme-to-protein assignment to make computed values concrete.
What you can do after
Build it from scratch by specifying a causal DAG, exogenous inputs, and an assignment function for each endogenous variable.

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Causal Inference for Data Science

A variable within a causal model whose value is determined by relationships represented in the model and by associated exogenous variables.

How it is used
Organizes variables inside a causal model so their dependencies and external influences can be represented separately.
How deep it goes
Defines endogenous variables through parents and structural functions, but stops short of specifying those functions exactly.
Examples
Uses a three-node graph with Z, X, and Y, plus unobserved factors UZ, UX, and UY.
What you can do after
Evaluate average treatment effects even when the exact causal functions governing endogenous variables remain unknown.

Read how Causal Inference for Data Science explains endogenous variable →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.