Top Tweets for #CausalPy
Agentic Causal Inference is here! ๐
By connecting @cursor_ai to @marimo_io via MCP, Gemini 3 Pro can execute and iterate on CausalPy models in real-time. From automated sensitivity analysis to Bayesian transparency!
๐Watch: https://t.co/8is727FKg5
#CausalPy #AgenticAI
In #CausalPy 0.6.0, one of the biggest upgrades isnโt a model, itโs clearer docs, tutorials, and examples + a unique Bayesian lens on structural causal discovery using variable-selection priors in joint models.
๐งฉTry it on Github: https://t.co/VW1PdnvFLp
#BayesianModeling
๐ฅ CausalPy 0.5.0 is out! Now supports multiple treated units in synthetic control. A big boost for geo-lift analysis and more.
๐ Learn more: https://t.co/rL9F8zmoVI
Huge thanks to them for their great work! ๐
#CausalPy #CausalInference #PyMC
๐๏ธ In a recent discussion, @twiecki and @1010is10 reflected on their experiences building high-performing remote teams at @pymc_labs , sharing successes, challenges, and lessons learned. Hereโs a glimpse:
๐ https://t.co/GzqNtvyWW7
#Interview #PyMCMarketing #CausalPy
๐๏ธ In a recent chat, @ulfaslak reflected with @twiecki on moving from academia to industry.
๐Watch the full interview here: https://t.co/jN9aHx8NoO
#interview #PyMCMarketing #CausalPy
๐จ Heads up! Our latest Bayesian Newsletter just dropped! ๐ฌ
๐ https://t.co/ywm5bWHbGA
๐คฏ This monthโs edition is packed with exciting news! From our work with @CP_News to updates on PyMC-Marketing, CausalPy, upcoming events, and more!
#Newsletter #PyMCMarketing #CausalPy
๐ข ย In case you missed the live webinar on ๐๐ข๐ฆ๐ ๐๐๐ซ๐ข๐๐ฌ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ with @pymc_devs featuring Jesse Grabowski and @twiecki, the recording is now available on our YouTube channel.
๐ https://t.co/dwPhgdzoAc
#PyMCMarketing #CausalPy
โ
How can @pymc_devs help you build better Time Series Models?
๐ฏJoin us on Aug 26 for a deep dive into Time Series Analysis with Bayesian State Space Models.
๐คSpeaker: Jesse Grabowski
๐๏ธHost: @twiecki
๐ Register: https://t.co/AcrWUeXhmH
#PyMC #PyMCMarketing #CausalPy
#CausalPy is a python package developed by
@inferencelab
that gives you the tools to analyse quasi-/ natural-experiments. Tune in to episode 87 to learn more or read the docs here: https://t.co/bLCaSGyCHR
๐ Say hello to the new #CausalPy logo! ๐ฅ
๐ Check it out on our GitHub and docs pages. ๐
๐ https://t.co/T2JBO6eA5a
๐ https://t.co/hvg73rNvOC
#Python #DataScience #CausalInference #NewLogo
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