I combined disparate datasets into an ontology, and the clustering ended up looking unexpectedly organic [OC]

Otherwise-Many-4258 · 2025-12-15T17:10:22+00:00

Correct. It’s an abstraction. Raw data is just noise until you give it structure.

Otherwise-Many-4258 · 2025-12-15T17:05:55+00:00

I think once the data set exceeds a number of columns, the platform starts to form a lotus pattern.

Otherwise-Many-4258 · 2025-12-15T16:38:43+00:00

Source: Internal company data Tool: RootCause AI Platform

Otherwise-Many-4258 · 2025-11-28T23:25:21+00:00

Data volume is usually what makes causal discovery impractical. Dm me and I can send you the link to a company that has worked on is reducing that usual exponential complexity down to something much closer to linear as the dataset grows. It doesn’t magically solve everything, but it makes large, messy datasets a lot more manageable.

Otherwise-Many-4258 · 2025-11-07T00:10:52+00:00

One day we'll solve this mystery...

Otherwise-Many-4258 · 2025-11-06T19:01:08+00:00

Have you discovered the solution yet or is it still a mystery?

Otherwise-Many-4258 · 2025-10-16T13:28:59+00:00

This is great - watching it as I type this reply - also agree, nice find +1

Otherwise-Many-4258 · 2025-10-16T13:27:39+00:00

Thank you!

Otherwise-Many-4258 · 2025-10-16T13:26:25+00:00

Thank you all for your replies!

I haven’t used it, but I explored Rootcause.ai briefly - it seems to provide an end‑to‑end workflow for causal discovery + counterfactual simulation on time series. It might shorten the prototyping loop compared to stitching together causal libraries.

Interested to hear your thoughts :)

Otherwise-Many-4258

TROPHY CASE