xinzhuwang-wxz

xinzhuwang-wxz / OpenPE

Public

Principle to Endgame

45
4
100% credibility
Found Apr 09, 2026 at 45 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenPE is an open-source framework that autonomously performs first-principles causal analysis by generating hypotheses, acquiring data, testing causal links with refutations, projecting scenarios, and producing verifiable audit trails.

How It Works

1
🔍 Discover OpenPE

You hear about OpenPE, a helpful tool that investigates tough questions like whether China's Double Reduction policy really cut family education costs.

2
❓ Ask your question

You simply write your question in plain words, like 'Did the policy truly reduce household spending on education?' and pick the topic area.

3
📁 Set up your folder

With a few clicks, you create a special folder for your question where everything will happen.

4
🚀 Let it explore and analyze

OpenPE automatically gathers facts, checks data, runs tests, and builds a picture of what might be causing what, all while you wait for exciting results.

5
📈 Review the findings

You see clear charts, tests that check if links hold up, and honest scores on how sure we can be about each connection.

✅ Get your complete report

You receive a full story with causal maps, future scenarios, and an honest trail from your question to the answers, ready to share or act on.

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Star Growth

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AI-Generated Review

What is OpenPE?

OpenPE is a Python framework that automates causal analysis from first principles to endgame: feed it a question like "Did China's Double Reduction policy cut household education spending?", and it generates competing causal DAGs, fetches data, runs refutation tests, projects scenarios, and outputs a report with quantified confidence, EP decay charts, and a verifiable audit trail. Powered by Claude Code for orchestration and tools like DoWhy for inference, it enforces stopping rules via multiplicative explanatory power (EP) decay, preventing endless chasing of weak chains. Users get autonomous reports classifying projections as robust, fork-dependent, equilibrium, or unstable—no manual model tweaking required.

Why is it gaining traction?

Unlike standard causal tools that trust your DAG or spit out p-values without boundaries, OpenPE pits user hypotheses against alternatives with no privilege, uses EP truncation for an emergent analytical horizon, and delivers machine-checkable audits from claims to sources. The one-shot Claude Code integration hooks devs: paste a query, get a full pipeline scaffolded and run. It generalizes github principle thinking to any domain, blending LLM autonomy with rigorous refutation batteries.

Who should use this?

Policy analysts probing interventions (e.g., "does X cause Y?"), economists tracing chains like free energy principle github analogs in macro data, or researchers needing reproducible endgame projections without confirmation bias. Ideal for single responsibility principle github workflows where you want principle-to-endgame traceability over ad-hoc scripts.

Verdict

Try it for structured causal deep dives—promising GPL-3.0 framework with solid EP mechanics and phase-gated verification, but at 45 stars and 1.0% credibility, it's early-stage; expect scaffolding tweaks and await more examples beyond education policy. Solid for principle enthusiasts, skip if you need battle-tested production scale.

(198 words)

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