CausalGuard 🛡️

The data quality guardian for causal inference & fraud detection
Catch flipped labels, temporal leaks, and causal biases before they destroy your model — in 5 lines of code.

The bugs that quietly kill 40–60% of fraud & marketing models:

• first_purchase_ts < signup_ts in 38% of rows
• Flipped fraud labels from bad annotation
• Treatment applied before user even existed
• Collider bias opening backdoor paths
• Silent distribution shift in causal features

I’ve personally lost months and millions because of these. Never again.

One-liner that saves you:

from causalguard import CausalGuard

report = CausalGuard(preset="fraud").scan(df)
report.show()  # → instant beautiful HTML report with fixes