CausalPy
CausalPy, from PyMC Labs, estimates causal effects from quasi-experiments: a price change, a launch in one market, a campaign that ran in some regions only.
What it adds to your workspace
- Synthetic control, difference-in-differences and regression discontinuity
- Bayesian uncertainty on every effect
- Works on any metric in your warehouse
Install
$ segmentstream add causalpy
▸ files added to your workspace repo
▸ deploy to start
Or ask your agent
“Estimate the effect of our June price change with synthetic control.”
Pricing
Open source. Runs on platform usage; no licence fee.
Part of these solutions
More extensions
Want CausalPy set up for you?
Our forward-deployed engineers build one solution on your data, in your own workspace, as code you own. Tell us what you run today and what you want; your note goes straight to our founder.
- One solution, on your data, in your own workspace
- First version live in two weeks
- Parity proven before anything is switched off
- Fixed scope and price, credited to your contract if you continue
Not ready for a pilot? Send the form anyway with your question. An engineer will reply, not a sales rep.
How the pilot worksYour company’s context, ready for every agent.
$ segmentstream workspace create acme --region eu
▸ workspace ready · data, objects, metrics and access rules in one place
▸ connect Claude, ChatGPT or Cursor with one connector
▸ tell your agent: “add our billing API as a source”
Our engineers build the first version on your data in two weeks, at a fixed price.
Or join the waitlist for self-serve:
Pilots are open now. Self-serve opens to the waitlist this fall.