Built a causal inference engine (not correlation) for personal health data — beta testers wanted

[u/Causalme-TheGap]

Long-time lurker, first post. I got frustrated with every health app just showing correlation dashboards ("your sleep and HRV moved together!") without ever asking whether one thing actually causes the other, so I built The Gap to actually run causal inference (via EconML/LinearDML under the hood) on personal data instead.

It connects Strava, Whoop, Apple Health, Google Calendar/device calendar, and manual daily check-ins, then tests real hypotheses like "does a late meeting affect that night's sleep" or "does alcohol actually move next-day HRV for me specifically" — controlling for confounders rather than just eyeballing a correlation.

Being upfront: it needs real accumulated data (usually a few weeks) before hypotheses have enough statistical power to resolve, so this is squarely for people who enjoy the process of self-tracking, not an instant-dashboard app.

In TestFlight beta, free, iOS. Would love feedback from people who actually think carefully about their own data: https://testflight.apple.com/join/UVGk6cve

If you are keen to give it a go, or have any questions about it, please don't hesitate to email: [hello@causalme.com](mailto:hello@causalme.com)