Empirical efficiency maximisation: improved locally efficient covariate adjustment
Seminar Room 2, Newton Institute Gatehouse
It has long been recognized that covariate adjustment can increase precision in randomized experiments, even when it is not strictly necessary. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved when modern studies collect copious amounts of baseline information on each subject. This dilemma helped motivate locally efficient estimation, in which one attempts to gain efficiency through a (possibly misspecified) working model. However, with complex high-dimensional covariates, where one might have no belief in the working model, misspecification can actually decrease precision. We propose a new method, empirical efficiency maximization, to target the working model element minimizing asymptotic variance for the resulting parameter estimate, whether or not the working model is (approximately) correct. Gains are demonstrated relative to standard locally efficient estimators.