🤖 AI Summary
This paper investigates the efficiency of fine-grained stratified randomized experimental designs for estimating generalized moment-based causal parameters—including average, quantile, and local average treatment effects. We propose a design framework that partitions experimental units into fixed-size groups and assigns binary treatments within each group. We establish, for the first time, that the naive moment estimator under this design achieves asymptotically optimal efficiency; we derive a rigorous efficiency lower bound and show that “rapid balancing” is a necessary condition for attaining it. Leveraging asymptotic statistical theory and regular estimator analysis, we demonstrate that this estimator achieves precision comparable to sophisticated post-hoc covariate adjustment methods—without requiring any such adjustment. Its optimality arises intrinsically from the experimental design itself. The core contribution is the formal establishment of fine-grained stratified design as an efficient, parsimonious, and design-driven paradigm for causal inference.
📝 Abstract
This paper studies the use of finely stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a"finely stratified"design, we mean experiments in which units are divided into groups of a fixed size and a proportion within each group is assigned to a binary treatment uniformly at random. The class of parameters considered are those that can be expressed as the solution to a set of moment conditions constructed using a known function of the observed data. They include, among other things, average treatment effects, quantile treatment effects, and local average treatment effects as well as the counterparts to these quantities in experiments in which the unit is itself a cluster. In this setting, we establish three results. First, we show that under a finely stratified design, the naive method of moments estimator achieves the same asymptotic variance as what could typically be attained under alternative treatment assignment mechanisms only through ex post covariate adjustment. Second, we argue that the naive method of moments estimator under a finely stratified design is asymptotically efficient by deriving a lower bound on the asymptotic variance of regular estimators of the parameter of interest in the form of a convolution theorem. In this sense, finely stratified experiments are attractive because they lead to efficient estimators of treatment effect parameters"by design."Finally, we strengthen this conclusion by establishing conditions under which a"fast-balancing"property of finely stratified designs is in fact necessary for the naive method of moments estimator to attain the efficiency bound.
