# The Science of Causal Inference in Personal Health

Causal combines Apple Health biometrics with randomized micro-sampling to estimate personal wellness patterns and reduce recall bias.

## Why Manual Diaries Fail

Manual health journals are affected by recall bias, selection bias, and logging fatigue. People may forget how they felt earlier, log mostly when they feel unusually good or bad, or stop logging because diaries are too much work.

Causal uses ecological momentary assessment: short prompts near the time of experience.

## Confounders

A confounder is a variable that influences both the supposed cause and effect. In health tracking, stress might lead to drinking more coffee and sleeping poorly. A simple correlation might blame caffeine for poor sleep while missing stress as a hidden driver.

## Causal's Framing

Causal maps logged factors and biometric outcomes into statistical models that estimate possible relationships between lifestyle factors and wellness outcomes.

These estimates are personal wellness signals, not medical proof or diagnosis.

## Canonical Page

https://causal.fitness/science