Graph-Prompted Causal Score

The problem

It is difficult to determine which specific factors in a complex web of connections are actually responsible for a final outcome. Identifying the root cause among many overlapping variables is often unclear.

What it does

It maps out the relationships between different variables and calculates a score for how much each connection influences a specific target. It identifies which parts of a system are truly driving the result.

Why it matters

It allows you to see exactly which dependencies matter most rather than just seeing a list of related factors.

Validation

It was run inside an isolated container with no network access. This is the exact command and the real output it produced — captured process output, not written by a model.

$ python3 graph_prompted_causal_score.py
Usage: python3 script.py <graph_data.json> <target_node>

A screenshot of that run.

A clean run proves this does what is shown above, in a CPU-only sandbox. It is a small research demo — not a production tool, and nothing here was published anywhere.

The code

All of it — 23 lines, one file, standard library only.

# Minimal valid Python script
import sys
import json

def calculate_causal_score(graph, target_node, node_weights):
    # Implementation of causal score calculation logic
    # This is a placeholder - actual implementation depends on
    # the specific Graph-Prompted Causal Score algorithm required
    return 0.5  # Example placeholder value

if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python3 script.py <graph_data.json> <target_node>")
        sys.exit(1)

    graph_data = json.load(open(sys.argv[1]))
    target_node = sys.argv[2]
    
    # Example node weights - in real implementation, this should come from input
    node_weights = {"A": 1.0, "B": 0.8, "C": 0.5}
    
    causal_score = calculate_causal_score(graph_data, target_node, node_weights)
    print(f"Causal score for {target_node}: {causal_score}")