Causal Inference
A lightweight causal layer for predicting action outcomes by modeling interventions and counterfactuals, rather than pattern-matching correlations. Enables causally valid planning with explicit audit trails and uncertainty quantification.
🚀 Causal Inference predicts what will actually happen when you take action—not by guessing patterns, but by modeling real cause-and-effect. Every action (send email, schedule meeting, post update) gets logged with its context, outcome, and confidence level. You get a clear audit trail showing why something worked or failed.
💡 Use this for smart decision-making: Should you send that message now or wait? Will this calendar change cause conflicts? What's the real impact of your actions over time? Works across email, calendar, tasks, files, messages, and more. Automatically learns from your history to improve predictions.
✨ Unlike pattern-matching tools, this builds a causal model of your actions—meaning you can actually debug failures, replay scenarios, and make decisions based on real cause-and-effect, not just correlation.
Requirements
Core causal modeling and inference library
Python runtime for backfill scripts and data processing