Top Tweets for #DoWhy
@yudapearl @RajWebshar Highly recommend @AleksanderMolak ‘s book for hands on in @amt_shrma et al #dowhy package and vignettes have been very helpful as well. Both in #python but easily usable in #rstats through #reticulate package. #bnlearn is another good Bayesian network one
@anirbanb_007 Thank you @amt_shrma et al for the awesome #dowhy package! Very easy to use in both #Python and #RStats via #reticulate
If you're interested in joining too, we have some easier "first issues" that need tackling, incl. bug fixes, enhancement requests, and doc improvements for #dowhy at: https://t.co/dPvXTETODz
Or, join us on discord at https://t.co/CV4ml0yC27
This is a great time (*) to thank the many contributors to the #dowhy library for causal inference https://t.co/ibCK9Q7yC6
(*) it's always a great time to thank people
#causality #causalinference
Check out #DoWhy 0.9. Now with improved nonparametric modeling via @autogluon. Thanks to @PatrickBloebaum @kailashbuki and the AWS Tübingen Causality team for all the great work! https://t.co/gAmYJwYJUO
#AWS contribue à la création de nouveaux #algorithmes d'#apprentissageautomatique #ML causal à la bibliothèque #DoWhy #Python 👏 https://t.co/SPrZ3Elgzt
"#AWS immediately contributed a series of algorithms to #DoWhy based on Judea Pearl’s graphical causal models (GCMs), which are able to visually model causal relationships between variables in an inference system." @yudapearl
"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
Causal Inference : DoWhy + EconML
https://t.co/Fifb9oQeVE
#CausalInference #CausalModel #DoWhy #EconML #GML #MachineLearning
The talk of @parisba about how to simulate your #robot brain with game engine really made us think even more. Also @AmitSharma gave us a tons of things to reconsider with his causal principles for explaining machine learning, a session with #DoWhy and #DiCE libraries talk.
.@amt_shrma presents #DoWhy, a library for causal inference at #CDSM20;
On top of identification and estimation it comes with a suite of robustness checks that allow to see what your model will say under different hypothetical violations of your assumptions. Very cool!
Microsoft’s DoWhy is a Cool Framework for Causal Inference https://t.co/IFfG0AN8qu #machinelearning #deeplearning #datascience #artificialintelligence #TheSEquence #invectorlabs #DoWhy #MSFTResearch
Three years ago @emrek and I started #dowhy python library for causal inference.
This podcast tells the story of how it started and the continuing challenges of identification, estimation & counterfactual prediction. Thanks @paul_conyngham for featuring us https://t.co/aaIb8pbhRY
Microsoft DoWhy is an Open Source Framework for Causal Reasoning https://t.co/nF30NBckUZ #DeepLearning #MachineLearning #DataScience #ArtificialIntelligence #Microsoft #DoWhy #InvectorLabs
Our #DoWhy #causalinference library has an awesome new API, courtesy of @akelleh!
Main idea: The do-sampler API transforms a dataframe of observational data into a dataframe that estimates the interventional distribution.
Read all about it:
https://t.co/fmSA55rKLe
I just published Introducing the do-sampler for causal inference https://t.co/7xYBxOKbTr
Mine and @JECliffy cover of Drinkin’ by Holly Williams🥃 #Drinkin @hollywilliams1 #rebeccakatemusic #dowhy
Test to see if #DoWhy library for causal inference by @emrek @amt_shrma is easy to use in @AzureNotebooks @ProjectJupyter notebook. Installs & runs fine: https://t.co/JznBdsCW9a (apart from pygraphviz unsupported for the graph plots). @msdev
Microsoft’s #DoWhy is an #OpenSource #Python-based library for #CausalInference and analysis that attempts to streamline the adoption of causal reasoning in #MachineLearning applications https://t.co/GJd692yd1B
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