Kausyn brings causal AI to the time series behind your decisions — separating the real drivers from correlations that don't hold, across finance, mobility, healthcare, energy and beyond.
Correlation tells you what moved together. Causality tells you what to change.
Two gaps sit between raw time series and a decision you can defend. Kausyn is built to close both.
Variables that move together look identical whether one causes the other or a hidden factor moves both. Models that exploit the pattern give confident answers that fail when conditions change — and offer no lever to act on.
Correlations spike in a crisis, demand shifts across seasons, physiology changes with treatment. Tools that assume a fixed structure blur these regimes into one misleading average. The change itself carries information — most methods discard it.
Kausyn recovers the direction and structure of cause and effect from observational time series — and holds up in the real-world conditions where data drifts and rules shift.
Recovers directed cause-and-effect structure from observational data alone — no controlled experiments or interventional trials required.
Built to stay reliable through drift, regime shifts, and changing volatility — the real-world instability that quietly defeats conventional methods.
Every recovered relationship passes statistical error control, so a link is stated when the evidence supports it — not because a number looked large.
Multivariate observational time series go in as recorded — no need to pre-clean them into an artificially stable shape first.
The method reads how each variable behaves over time to resolve which variables drive which, and in what direction.
Out comes a directed causal graph with error control applied — the drivers behind the system, and where to focus.
Causal discovery is horizontal. Anywhere decisions rest on time series, the same question applies: what is actually driving this. These are the sectors Kausyn is focused on.
Lead–lag structure and exposure between instruments and institutions through shifting correlations.
Drivers of demand, delay, and flow across networks, where load and behaviour change constantly.
Physiological couplings and treatment effects read from monitored signals over time.
Relationships that move with seasons and events, from grid load to environmental drivers.
What truly moves demand and response, past the correlations a dashboard would report.
Root drivers behind quality, throughput, and failure in sensor-rich industrial processes.
Kausyn is an early-stage causal-AI company based in Morocco. We develop our core methods ourselves and prove them against established benchmarks before putting them to work on real systems. The near-term focus is turning that foundation into results on real-world data, one sector at a time.
We work with data partners, collaborators, and research groups. The best place to start is a real dataset where getting cause and effect right is worth it.