Syndicate content

randomization inference

Finally, a way to do easy randomization inference in Stata!

David McKenzie's picture

Randomization inference has been increasingly recommended as a way of analyzing data from randomized experiments, especially in samples with a small number of observations, with clustered randomization, or with high leverage (see for example Alwyn Young’s paper, and the books by Imbens and Rubin, and Gerber and Green). However, one of the barriers to widespread usage in development economics has been that, to date, no simple commands for implementing this in Stata have been available, requiring authors to program from scratch.

This has now changed with a new command ritest written by Simon Hess, a PhD student who I met just over a week ago at Goethe University in Frankfurt. This command is extremely simple to use, so I thought I would introduce it and share some tips after playing around with it a little. The Stata journal article is also now out.

How do I get this command?
Simply type findit ritest in Stata.
[edit: that will get the version from the Stata journal. However, to get the most recent version with a couple of bug fixes noted below, type

net describe ritest, from(https://raw.githubusercontent.com/simonheb/ritest/master/)

You ran a field experiment. Should you then run a regression?

Berk Ozler's picture
Recently, a colleague came over for dinner and made the following statement: “Person X told me that Imbens is now saying that we should not be running regressions to estimate average treatment effects in experiments.” When I showed some sympathy for this statement while focusing more on making tortillas, she was resistant: it was clear she did not want to give up on regression models…

What does Alwyn Young’s paper mean for analysis of experiments?

David McKenzie's picture

I’ve been asked several times what I think of Alwyn Young’s recent working paper “Channelling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results”. After reading the paper several times and reflecting on it, I thought I would share some thoughts, with a particular emphasis on what I think it means for people analyzing experimental data going forward.