MENTAL MODEL · M3659

Causal Layered Analysis, CLA

SystemsHigh supportSystems Theory

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Updated 2026-08-05

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INTRODUCTION

English translation pending.

CORE DEFINITION

Causal Layered Analysis, developed by Sohail Inayatullah, dissects an issue at four levels: the litany of headline data, the social and systemic causes, the worldview or discourse that frames it, and the deep myth or metaphor beneath. Lasting change requires working at the deeper layers, not only at the surface of numbers.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Depth unpacking: push past headline statistics down to systemic and cultural causes. - Frame checking: name the worldview that makes a given policy feel obvious. - Root leverage: change the underlying myth or metaphor to change outcomes.

anchor

Anchor fast decisions

Stratified analysis splits a population by confounding variables, compares exposure and outcome within each stratum, and then pools the stratum-specific estimates. Because the confounders are held constant inside each stratum, the pooled result approximates an unconfounded causal effect and can overturn a misleading overall comparison.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

Identify plausible confounders before comparing exposure with outcome. 2. Split the sample into strata defined by those confounders. 3. Estimate the exposure effect separately inside each stratum of the data. 4. Check whether the effect is consistent across strata or modified by them. 5. Pool the estimates, for example with Mantel-Haenszel methods, and reduce residual confounding where possible.

Check to track your progress (stored locally)

Learning progress0%

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Source support: Explicit

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en.wikipedia.orghttps://en.wikipedia.org/wiki/Causal_layered_analysisZH · Explicit
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