The statistical literature on causal inference is based on notation that expresses the idea that a causal relationship sustains a counterfactual conditional (e.g, to say that taking the pill caused John to get better means John took the pill and got better and that had he not taken the pill, he would not have gotten better). Using this notation, causal estimands are defined and methods used to estimate these are evaluated for bias.
This talk is to introduce you to this notation and literature and to point to some issues such as mediation and interference that have been addressed (at least somewhat) in the literature that may be of interest and relevance to neuroscience.
Tuesday, December 4, 2012
Tomorrow at 1PM I'm going to present some overview of the recent work on approximate message passing algorithms (AMP) with applications to compressed sensing (CS).
I'm going to start with a brief overview of message passing algorithms  and then show how it was used in  to derive an AMP algorithm for the standard CS setup (basis pursuit, lasso).
The time permitting I'm going to briefly present some extensions of this methodology to the case of more general graphical models .
Material will be drawn from the following sources:
by liam at 10:14 PM