casuallite 0.1.0

CasualLite: The world's first Hybrid Meta Causal Learning Algorithm

🔬 CasualLite — Generated by Project GENESIS

⚠️ This is not a human-coded project.
CasualLite was entirely discovered and built by Project GENESIS — our autonomous AI engine for AI research.

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🧠 What is CasualLite?

CasualLite (coded as RCSE-IBFS) is a fast, interpretable causal discovery algorithm created by GENESIS. It builds causal skeletons from tabular datasets using independence-based and scoring-based mechanisms.

This repository is a proof-of-concept to demonstrate the reasoning, architecture design, and implementation capabilities of GENESIS.

We are proud to announce that CasualLite is the first public algorithm from Project GENESIS under the banner of theAiAlgoRepository.

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🤖 Built by GENESIS: What does that mean?

Project GENESIS is an AI research system capable of autonomously producing original machine learning algorithms — from concept to functional code — without human programming.
For CasualLite, GENESIS handled the full research and development lifecycle, delivering the final implementation in 50 seconds.

Human involvement in code creation: zero.

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⚙️ CasualLite Highlights

• Zero black-box dependencies
• Causal skeleton recovery in seconds
• Scales to datasets with 10–50+ variables
• Minimal GPUs or tuning required
• Outputs adjacency matrix and visual graphs

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🧪 Usage

import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from casuallite import RCSE_IBFS, plot_graph
import seaborn as sns
import numpy as np


if __name__ == '__main__':
    # Load and preprocess the Titanic dataset
    df = sns.load_dataset("titanic")
    df['sex'] = df['sex'].map({'male': 0, 'female': 1})
    df['embarked'] = df['embarked'].astype('category').cat.codes  
    df['class'] = df['class'].astype('category').cat.codes
    df['who'] = df['who'].astype('category').cat.codes
    df['deck'] = df['deck'].astype('category').cat.codes
    df['alive'] = df['alive'].map({'no': 0, 'yes': 1})
    df['adult_male'] = df['adult_male'].astype(int)
    df['alone'] = df['alone'].astype(int)
    df = df.select_dtypes(include=np.number).dropna()

    X_data = df.to_numpy()
    feature_names = df.columns.tolist()

    print("\n🚀 Running RCSE-IBFS...")
    # Correctly unpack the two return values
    G, importances = RCSE_IBFS(X_data, beta=0.9, alpha_init=0.15, max_cond_set_size=2, feature_names=feature_names)

    print("\n📌 Recovered Causal Skeleton:")
    if G and G.nodes():
        for node in G.nodes():
            print(f"{feature_names[node]}: {[feature_names[n] for n in G.neighbors(node)]}")
        # Pass the importances dictionary to the plotting function
        plot_graph(G, labels=feature_names, title="Titanic Causal Skeleton (RCSE-IBFS)", node_importance=importances)
    else:
        
        print("No causal edges discovered.")

🧭 Why This Matters

This marks a turning point: CasualLite is one of the first fully AI-generated causal discovery algorithms released to the public.

This isn’t AutoML. This is AutoScience — the beginning of AI engines performing real scientific work.

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🧑‍💻 GENESIS Created. You Deploy.

Use CasualLite in your own pipelines. Or, use GENESIS to build your own algorithm.

For partnership or licensing inquiries: [Email us] [e-mail] or [e-mail]

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📄 License

Apache 2.0 License

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🤖 About Project Genesis and theAiAlgoRepository

GENESIS is an AI-driven research system that has already produced groundbreaking machine learning algorithms.

Its milestones include ARF-OP (Adaptive Resonance Forest with Optimized Pruning), MSGL-v2.0 (Meta-Structural Graph Learner), and its first publicly released algorithm — CasualLite — a lightweight, high-speed causal discovery algorithm.

theAiAlgoRepository is a dedicated branch of the GENESIS ecosystem, serving as the official channel for accessing and acquiring both public and commercial algorithms developed by Project GENESIS.

It also offers premium Research-as-a-Service (RaaS) solutions for academic, industrial, and enterprise applications. Institutions, researchers, AI labs, and corporations can engage with theAiAlgoRepository for licensed, commercial use of GENESIS-developed technologies.

GENESIS continues to set new benchmarks in AI-led algorithm invention and innovation.

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