2) I can't remember exactly how much, but the registration fee was certainly in the order of hundreds of dollars and it had to be paid when submitting the abstract, several months before the conference; and they were giving no refund if your abstract wasn't accepted, either (although, given how many talks there actually are, probably no abstract was rejected). So, I think it's really not great that they don't provide any food at all (there is a canteen, but you have to pay on top of the registration). And no coffee breaks or notepads either.

3) Causal inference is

**really**big and there are at least 3-5 sessions a day on this topic. I've been to a few of them and in general it was all good stuff. But what has struck me is that essentially

*all*(and I mean:

*all*) the presenters dealt with it using potential outcomes. I know that's the gold standard, but what I have never got (well, since I've first read about all this back in 2003, that is) is how people can be so fully committed to counterfactuals as the only way to frame causal inference.

Recently, I re-read chapters 9-10 of Gelman-Hill (which, by the way, is a great book). I quite like how they make the distinction between

*standard regression methods*(aimed at comparisons between

*two*different individuals, who happen to possess the exact same characteristics apart from a treatment or exposure of interest) and

*regression models for causal inference*, which concern a comparison of the effects of the interventions for the same individual. However, they too describe this in terms of counterfactuals, ie

*what would have happened*to unit $i$ if, counter to the observed facts, they were given treatment $t=0$ instead of $t=1$.

I personally still think that it makes more sense to see causal effects in terms of what

*will happen*to unit $i$ if in the future they will get treatment $t=0$ instead of $t=1$. Hopefully we'll be able to some applied work in a non-counterfactual framework for the RDD stuff, if we get our grant.