Getting Started
Create an account, upload your first dataset and run a causal discovery. The workflow goes from data upload to an interactive causal graph you can explore, test and export.
Documentation
How to get from a dataset to a causal graph you can act on.
Sign up; your organisation workspace is created with your account on the Free plan.
Bring a CSV or JSON file (save Excel sheets as CSV first). Columns become candidate causes and effects — no labelling needed.
Pick an algorithm and threshold, then read the graph: every edge carries a signed weight, and a confidence score when the algorithm measures one.
Post a table of observations and get back the edges the engine could defend, each with a signed weight, and a confidence score when the algorithm measures one.
curl -X POST "$CESTRUM_API_URL/api/causal/run" \
-H "X-Api-Key: $CESTRUM_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"datasetName": "pricing",
"modelType": "PC",
"variableNames": ["price", "demand", "inventory"],
"data": [
[12.5, 840, 1200],
[11.0, 910, 1040]
]
}'Create an account, upload your first dataset and run a causal discovery. The workflow goes from data upload to an interactive causal graph you can explore, test and export.
How UCMT discovers causal structure from observational data: how to read an edge’s signed weight and, where the algorithm measured one, its confidence, and how to check results against what you already know about the domain.
How to test an intervention on a discovered graph: choose the variable to change, set its value, and read the estimated effect on the variables downstream of it.
Guidelines for preparing data for causal discovery: handling missing values, encoding categorical variables, and making sure you have enough rows for the relationships you want to identify.
Calling the platform programmatically: authenticate, upload a dataset, start a discovery run and fetch the resulting graph. The API reference lists the endpoints.
Send us the dataset shape and what you expected — we answer technical questions directly.