Correlation, Causation and Research Design

ECONORIA Evidence Lab 06

EVIDENCE LAB · 06

Correlation
& Causation.

Patterns describe what moves together. Causal analysis asks what would have happened otherwise—and demands a credible design for answering it.

06 Laboratory70 MinutesΔ Causal effect
Y₁−Y₀EFFECT

TREATMENTCONTROLDESIGN

01 · THE CAUSAL LADDER

Association is evidence.
Not yet an effect.

LEVEL 1

Description

What happened, to whom, where and when?

E[Y]

LEVEL 2

Association

How does outcome Y vary when exposure X varies?

Corr(X,Y)

LEVEL 3

Causation

How would Y change if X were deliberately changed?

E[Y(1)−Y(0)]

OBSERVED WORLD

Y(1): treated outcome

What happened after the person, firm or region received the intervention.

UNOBSERVED ALTERNATIVE

Y(0): untreated outcome

What would have happened to that same unit at the same time without treatment.

02 · THREATS TO IDENTIFICATION

Three routes from pattern
to false conclusion.

Z

Confounding

A third factor influences both X and Y. Ice-cream sales do not cause drowning; hot weather raises both.

Reverse causality

Y may affect X. Growth can attract investment even when investment also influences growth.

S

Selection bias

Treated and untreated units may differ before treatment in ways that also affect the outcome.

CAUSAL DESIGN STUDIO

Build the
counterfactual.

Define the intervention, outcome, comparison and assignment mechanism. ECONORIA will audit the identification logic.

IDENTIFICATION BLUEPRINT0%

Define the treatment and outcome.

Treatment
Not defined
Outcome
Not defined
Counterfactual proxy
Not defined
Design
Observational comparison
Key threat
Unobserved differences between groups

CAUSAL VERDICTA descriptive association can be estimated, but no credible causal claim is yet supported.

DESIGN CREDIBILITY 0%

03 · IDENTIFICATION STRATEGIES

The strongest design is the one
whose assumptions survive scrutiny.

Randomised experiment

Assignment by chance creates comparable groups in expectation.

Regression discontinuity

Compare units narrowly around a policy eligibility threshold.

Difference-in-differences

Compare changes over time between treated and comparison groups.

Instrumental variables

Use external variation affecting treatment but not the outcome directly.

Complete Laboratory 06 →

EECONORIAEconomic knowledge for a changing world.

Design before claiming.

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