Experimental Designs with Unequal Variance Across Treated and Control Data

While I generally do not dabble all that much in experiments, I am familiar enough with the literature to know that, aside from general statistical power concerns, ex ante questions about how many subjects should be in the treated and control groups elude easy answers. One variant to this question considers when a researcher should have unequal numbers of units in the treatment and control groups. To be sure, such practical factors as a treatment's cost and/or disruption surely play some role. A recent discussion on Andrew Gelman's blog (here) takes up more complicated issues relating to how best to maximize the statistical efficiency of treated and control subjects' distribution and describes how simulations might assist.