@tdietterich @yudapearl That makes a lot of sense to me. Still there seems to be a lack of clarity around how variables should be chosen, which choices are valid, which are useful. See e.g. https://t.co/OWSaOYFZTq https://t.co/IoOdprp6qs For CRL one woul
..and probabilistic models https://t.co/0wGOtX6YfL If variables are not properly defined/constructed (cf. above), we may run into problems of ambiguous manipulations https://t.co/GUUVzvAsA9 and green and grue variables https://t.co/Hc8GcoOcD5 2/
@atypical_me @KordingLab @neuroccino That is an excellent question :-) Some works at UAI 2017/18 addressing this from a conceptual methodological perspective by Beckers and Halpern https://t.co/3FFNcvIHHG based on this https://t.co/iWaFzMYnJD; more here ht
@atypical_me @GunnarBlohm @KordingLab @dan_marinazzo This great article by Frederick Eberhardt may be a good starting point, instructive, illustrative, and well written https://t.co/Hc8GcoOcD5 "observations of some variables Xi, let's infer the causal str
@neuropoetic @KordingLab @anilkseth @micahgallen @dan_marinazzo @m_heilb @bttyeo @stefan_fraessle @corticalpete More core CI literature by Chalupka & Eberhardt et al on causal variable learning, transformations, micro/macro (again, not specifically foc
@neuroccino @KordingLab E.g. this fundamental question you're raising: How causal modelling abilities depend on the (aggregate) variable being used (or marginalised out)? https://t.co/M3gj07hTzt https://t.co/faEX3L7v6a https://t.co/iWaFzMYnJD https://t.co
But, forever, it will be M GO GRUE! :) http://t.co/qjBOAYIP8v