Cognitive Scaffold

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MENTAL MODEL · M13163

Causal Models

Causal Models
TechnicalHigh supportScientific Methodology
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Updated 2026-09-10

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INTRODUCTION

English translation pending.

CORE DEFINITION

Causal models represent the causal relations within a system or population mathematically, letting researchers infer causation from statistical data and clarifying the epistemology of causality and the relation between causation and probability. The framework, developed largely by Judea Pearl and collaborators, is also applied to the logic of counterfactuals, to decision theory, and to the analysis of actual causation.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Draw the graph: map variables as a directed graph so hidden causal assumptions become visible. - Separate seeing from doing: distinguish conditioning from intervening, and never answer intervention questions with observational data. - Test the model: compare graph-implied separations with data independencies to falsify wrong arrows.

anchor

Anchor fast decisions

Causal models encode more than a probability distribution: structural equations describe how each variable is determined by others, and an intervention replaces an equation rather than merely conditioning on a value. The Markov condition links separation in the graph to probabilistic independence, letting data test the model, while faithfulness and minimality constrain which graphs are admissible and block arbitrary arrows.

MINIMUM ACTION

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Source support: Explicit

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    plato.stanford.eduhttps://plato.stanford.edu/entries/causal-models/ZH · Explicit
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