This article by Mark Bittman has pretty interesting stuff on causality in medicine, given that it's impossible to run a randomized trial where you randomize a lifetime of smoking or eating lots of sugar.
Teaching is exhausting, but fun. This paper by Nunn & Puga from REStat last year is my favorite article I've taught so far this semester. They claim ruggedness is bad for GDP in most places of the world for obvious reasons (transport costs), but it's good for GDP in Africa because it reduced the slave trade, which is even worse. Really no fancy econometrics, just a simple design and lots of robustness checks. You don't want to run a regression of GDP on ruggedness because omitted variables will obviously bias your estimate, but it's perhaps less bad to run one on ruggedness, an Africa indicator, and the interaction of ruggedness and the Africa indicator. The coefficient on ruggedness is still biased, but the coefficient on the interaction is only biased if there are omitted variables correlated with GDP and differentially correlated with ruggedness in and out of Africa, or if there's an omitted variable correlated with ruggedness and differentially correlated with GDP in and out of Africa. Certainly still a possibility, but a more complicated story. They test for it with diamonds, soil fertility, malaria, and tropical diseases, among other things, and their estimate doesn't change very much. However, I made a homework assignment requiring students to add their own new variable as a robustness check, and a couple of them actually may have found something that drives out the results. Anyway, it's interesting.