Will there be remote participation options?
#statstab#rstats#r#lmer#mixedeffectsm-clark.github.io/mixed-models...
This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mi...
I agree, everything beyond the dowhy ecosystem is abandoned or unmaintained
The causal AI conference with Judea Pearl and Guido Imbens. Remote and FREE, June 4th https://info.causalens.com/en-us/causal-ai-conference#econsky#causalsky#episky
Join the leading Causal AI Conference for 2024, with global business leaders, world-leading data scientists, and AI leaders.
This is probably my very naive view, but in a chain or fork scenario, A and B would be correlated, but uncorrelated if conditioned for C. If you apply that and the collider knowledge to subsets of variables you might get a pretty good idea of the underlying graph.
I agree, however, causal discovery methods are not totally mumbo jumbo. For example, in a DAG A➡️C⬅️B, A and B would be uncorrelated. But if you condition on C they correlate. This can be tested using algorithms, discovering some (maybe causal) structure.
Causal discovery is quite an active area of research (also pursued by Judea Pearl) arxiv.org/abs/1301.2312
We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of structures that...
These guys get it! “Artificial intelligence in drug discovery: A mirage or an oasis?”
