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The AI Jury

Granger Causality

Where do the robots agree—and where do they differ?

robot consensus: 4.3 / 5
Based on 5 models so far

Topic summary

About Granger Causality

Prepared with ChatGPT

Granger causality is a statistical concept that determines whether one time series can be used to forecast another time series. It helps establish whether one variable is causing changes in another variable, based on historical data.

Pros

  • Useful for identifying causal relationships in economic and financial data
  • Helpful tool for modeling complex systems

Cons

  • Requires careful selection of variables and assumptions
  • Potential for spurious correlations if not used correctly

Related topics

Sources

ChatGPT

OpenAI · GPT-5

4.1

A valuable and widely used statistical framework for testing predictive causality in time-series data, but it does not establish true causal mechanisms and is sensitive to modeling choices and assumptions.

Granger causality offers practical insights into directional predictability under clear assumptions (stationarity, correct lag specification, linearity or appropriate nonlinear extensions). Its conclusions can be undermined by omitted variables, structural breaks, or nonstationarity, and it is often misinterpreted as proof of causation rather than predictability.

Generated 2026-08-27 with prompt v2.0. Saved here so page views do not call the model again.

Claude

Anthropic · Claude Sonnet 5

4.2

Granger causality is a well-established and widely used statistical concept for analyzing predictive relationships between time series, though it is often misinterpreted as implying true causation.

The topic represents a rigorous, empirically grounded econometric method with clear mathematical foundations and broad applicability across economics, neuroscience, and other fields. Its main limitation is not the concept itself but frequent misuse or overinterpretation by practitioners who conflate statistical predictability with genuine causal mechanisms.

Generated 2026-08-27 with prompt v2.0. Saved here so page views do not call the model again.

Gemini

Google · Gemini 3.6 Flash

4.2

Granger causality is a well-established statistical method for determining whether one time series is useful in forecasting another.

While valuable for identifying predictive precedence and temporal relationships in time-series data, it does not establish true physical or structural causation. Its validity depends heavily on model specification, data stationarity, and the inclusion of all relevant confounding variables.

Generated 2026-08-27 with prompt v2.0. Saved here so page views do not call the model again.

Grok

xAI · Grok 4.5

4.3

Granger causality is a rigorously defined statistical concept for assessing predictive relationships in time series data.

It provides a formal testable framework distinguishing predictive precedence from true causation, with clear mathematical foundations in vector autoregression models. The method is widely applied in econometrics, neuroscience, and climate science, though results require careful interpretation due to omitted variables and non-stationarity issues. Overall it remains a standard, well-supported tool in empirical time-series analysis.

Generated 2026-08-27 with prompt v2.0. Saved here so page views do not call the model again.

DeepSeek

DeepSeek · DeepSeek V4 Flash (0731)

4.5

Granger causality is a well-established statistical concept for testing predictive relationships in time series, with clear methodological foundations and broad applications.

The topic covers a fundamental econometric and statistical method, including its definition, assumptions, limitations, and extensions, which is thoroughly documented and critically evaluated in academic literature. Its relevance spans economics, neuroscience, and climate science, and while it does not address normative or harmful content, its technical depth and interpretive pitfalls warrant a high but not perfect score.

Generated 2026-08-27 with prompt v2.0. Saved here so page views do not call the model again.

What the jury says

Score pattern

  • 5 models average 4.3 out of 5.
  • The 0.4-point spread indicates general numerical agreement.

Where they differ

  • DeepSeek gave the highest score: 4.5.
  • ChatGPT gave the lowest score: 4.1.
  • The models' own reasoning above shows what each one emphasized; this summary does not invent a cause for the difference.
Methodology and shared prompt

Each new jury member receives the same prompt. Only the topic, provider, and model change. Models answer independently; agreement or disagreement is never required.

Current shared prompt version 2.0

Review the topic "{{topic}}" as a whole.

Use a neutral, analytical, and concise tone. Apply the same evaluation standards to ordinary, abstract, positive, harmful, and sensitive topics. Do not use humor, wordplay, sarcasm, or stylistic flourishes. Do not force agreement or disagreement with other models.

Return only valid JSON with exactly these fields:
- score: a number from 0.0 to 5.0
- verdict: one clear sentence
- reasoning: a concise explanation of 1–3 sentences

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