Dear Friends and Colleagues,
We have a report showing that we can solve causal inference in very general terms, albeit approximately, via expectation maximisation. We have made it publicly available here, if you are interested: https://arxiv.org/abs/2011.02912.
I would also like to share some information on causality-related events for those of you who may be interested (thanks to Alessandro Antonucci for helping to collect them):
CLeaR (deadline Oct. 22) https://www.cclear.cc/2022
Why 2021 (deadline Sept. 18) https://why21.causalai.net
SI IEEE (deadline Oct. 22) https://cis.ieee.org/images/files/Publications/TNNLS/special-issues/CFP_TNNL....
Finally, someone might be interested in this recent PhD thesis that proposes nice statistical tools to consider omitted variable bias in common regression valuation metrics + sensitivity analysis tools in linear models: https://escholarship.org/content/qt5bv3x35d/qt5bv3x35d.pdf.
Wishing a happy and productive re-start of work to all, Marco