9. Designing Causal Evaluations

Simple random assignment is easy to execute, but may not be desirable if crossovers or spillovers are a problem, and may not be as efficient as stratified random assignment.

The user-written gsample command in Stata makes different types of random assignment straightforward to implement.

It's a good idea to check that your randomization code produces the same random assignment each time that you run it. The cfout command makes it easy to compare two randomizations and check for discrepancies.

Balance tests are useful as a quick check for obvious bugs in your code, but be careful not to over-interpret the results: if you randomized treatment assignment, then any differences in baseline data between treatment arms are necessarily due to chance.

If you have any baseline or administrative data on relevant covariates for individuals in your evaluation sample, then stratifying is a no-brainer.

Clustered random assignment is often necessary due to program implementation and/or to minimize spillovers, but it greatly reduces statistical power.