This was a week of nerdily viral statistics posts on my blog. A few days ago I talked about the knee-jerk clustering of standard errors in so many papers, and whether we should ever do this in individually-randomized experiments.

Turns out a lot of you have opinions and answers. Thanks for that.

For me the best news was an email from Stanford econometrician Guido Imbens:

The bottom line is that you are right. You have a sample, collected in whatever way. You do a randomized experiment with randomization at the unit level. Then you do not need to cluster. Without doing any cluster adjustment your standard robust variance estimator will lead to the correct inferences for the average effect for the (convenience) sample you have.

He backed this up with a working paper of his with Alberto Abadie, Susan Athey, and Jeffrey Wooldridge. This is basically the Mount Rushmore of applied econometrics, so: game, set, and match?

Not entirely. The paper is not yet ready for circulation (wait for early 2016). So let me outline some of the other intuitions and proofs people argued.

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