Beyond Simple
Correlation.
Traditional health apps track correlations. If you sleep poorly on nights you log stress, they may imply stress is the cause. But personal wellness patterns are often obscured by hidden factors. Causal uses statistical models to estimate which logged factors may influence your biometrics.
1. The Flaw of Manual Diaries
Health journals rely on manual, retroactive logging. This introduces three severe biases that degrade data quality:
- Recall Bias: Trying to remember how you felt 8 hours ago is highly inaccurate. People tend to overlay their current mood onto the past.
- Selection Bias: You are far more likely to open a health app and log data when you are feeling exceptionally good or exceptionally bad, completely missing the baseline.
- Logging Fatigue: Detailed text diaries create enough friction that many people stop logging before useful patterns can emerge.
Causal solves this through Ecological Momentary Assessment (EMA). By sending randomized, 3-second prompts during waking hours, we capture clean, low-bias health inputs in real-time.
2. Adjusting for Confounders with Causal Graphs
In statistics, a confounder is an unmeasured variable that influences both the supposed cause and effect. For instance, cold weather can contribute to both high heating bills and dry skin, making bills and dry skin highly correlated, but heating bills do not cause dry skin.
In health tracking, stress is a massive confounder. High stress might lead to drinking more coffee and sleeping poorly. A standard correlation analysis would tell you caffeine is the primary culprit behind your poor sleep. Causal maps these variables into a Directed Acyclic Graph (DAG):
Causal estimates the possible direct effect of Caffeine on Sleep while accounting for the confounding influence of Stress.
Using Python-based backend models, Causal estimates relationships between your logged factors and biometric outcomes. These estimates are personal wellness signals, not medical proof or diagnosis.
3. Aggregated Group Percentiles
Causal can summarize your patterns against available reference ranges and app-derived benchmarks when enough data exists. Instead of telling you "caffeine is bad," the goal is to show whether your own check-ins and Apple Health trends suggest a meaningful pattern.