Two things moving together doesn't tell you which caused which, or whether a third thing caused both. KAIROS answers that from your own data.
Instead of assuming what drives what, KAIROS discovers the cause-and-effect structure from your own data. Constraint-based causal discovery (CPDAG).
It doesn't force relationships into a straight line; it tests them as they really are. Nonparametric conditional-independence testing.
The costliest mistake is blaming an effect for something a hidden factor caused. KAIROS flags it. Latent-confounder detection.
KAIROS is what lets Chai answer “why” about any decision the plane made. Attributes the other engines' outputs for Chai.
Our validation methodology is a walk-forward backtest against standard baselines with Diebold–Mariano significance testing — designed to run on your own history during onboarding so you can see how it performs before you rely on it.
These are the real methods behind KAIROS, each in one plain line. We name the method as a credibility signal — we don't publish the recipe.
Learns the cause-and-effect structure from your data rather than assuming it.
Doesn't assume relationships are linear.
Flags a hidden common cause instead of mistaking it for the driver.