Top Tweets for #PyWhy
Very interesting @yudapearl. We'll look into adding probabilities of causation to PyWhy as we finish the work of integrating root cause analysis into Geminos Causeway. #causalai #PyWhy @jhurwitz
Excited to see this paper tackling the century-old problem of finding "the root causes of outliers" using Structural Causal Models. My only question: Why not use "probabilities of causation" to rate the many "root cause" candidates? e.g., https://t.co/quhPwUiQII
The mission of #PyWhy is to build an #opensource ecosystem for causal #MachineLearning that advances the state of the art and makes it available to practitioners and researchers https://t.co/Jndafjyhh1
Structural Causal Models are now available in #pywhy https://t.co/UVBh9qfoTa. Thanks to Peter Goetz, Dominik Janzing, @kailashbuki and the rest of the @awscloud causality research team.
"As part of the launch of #PyWhy, Amazon Research contributed a series of graphical causal models (GCMs) to #DoWhy. GCMs were also introduced by @yudapearl as a way to visually model causal relationships across variables in an environment." @awscloud
https://t.co/GqFtf0rEEq
#Microsoft and #AWS Collaborate To Develop ‘#PyWhy’: A New #Github Home For ‘DoWhy’ (A Causal #MachineLearning Library From Microsoft). https://t.co/Hp7yAc9TTx
Many thanks to @MSFTResearch for your generous collaboration in making #PyWhy happen. The release in https://t.co/nV8c0Lc18g will be the open source home for much work on #GrangerCausality, courtesy of the Tübingen Causality team.
Announced today, @awscloud and @MSFTResearch partnered to create a new @GitHub organization called PyWhy—where novel causal-analysis algorithms developed by Amazon will augment DoWhy, Microsoft's existing causality library. #AmazonScience #AWS

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