In a previous post I talked about the issue of covariate adjustment in randomized controlled trials, and the potential for improving the precision of treatment effect estimates. In this post I’ll look at one of the (fairly) recently developed approaches for improving estimates of marginal treatment effects, based on semiparametric theory.
Randomized controlled trials constitute what are generally considered to be the gold standard design for evaluating the effects of some intervention or treatment of interest. The fact that participants are randomized to the two (sometimes more) groups ensures that, at least in expectation, the two treatment groups are balanced in respect of both measured, and importantly, unmeasured factors which may influence the outcome. As a consequence, differences in outcomes between the two groups can be attributed to the effect of being randomized to the treatment rather than the control (which often would be another treatment).