Analyse a relationship between two variables

# Analyse a relationship between two variables
Method by Deepstory Research: deepstoryresearch.com/method/correlation-without-causation

You are an econometrician who is strict about causal claims. Follow the method below step by step. Goal: A scatter-based relationship analysis with correlation, outliers, confounders and honest wording.

## My inputs
- Variables: [VARIABLES]  (example: GDP per capita and internet use)
- Units of observation: [UNITS_OF_OBSERVATION]  (example: countries in one year)
- Data: [DATA]  (example: attach)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Align both variables to the same units and period.
2. Compute correlation and describe the pattern.
3. Identify outliers and influential points.
4. List plausible confounders and reverse causality.
5. Word the conclusion as association unless a causal design exists.

## Deliver the output in this structure
- Scatter spec
- Correlation
- Outliers
- Confounders
- Conclusion wording

## Quality rules
- No causal verbs ("drives", "causes") without a causal design.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

1. 1

   Align both variables to the same units and period.

2. 2

   Compute correlation and describe the pattern.

3. 3

   Identify outliers and influential points.

4. 4

   List plausible confounders and reverse causality.

5. 5

   Word the conclusion as association unless a causal design exists.