Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference

TL;DR

This paper provides a formal, design-based framework for analyzing crossover designs, emphasizing causal effects, efficiency, and robustness, applicable across various design types and assumptions.

Contribution

It introduces a potential outcomes approach for causal inference in crossover designs and unifies least squares analysis with flexible specifications for improved robustness.

Findings

Provides a formal causal estimand framework for crossover designs.

Develops a unified least squares estimation method with robustness to model misspecification.

Offers practical guidelines for specifying models and variance estimation.

Abstract

Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However, standard analyses of crossover designs often rely on strong parametric models, making inference vulnerable to model misspecification. This paper adopts a design-based framework to analyze general crossover designs. We make two main contributions. First, we use potential outcomes to formally define the causal estimands and assumptions on the data-generating process. For any given type of crossover design and assumptions on potential outcomes, we outline a procedure for identification and estimation, emphasizing the central role of the treatment assignment mechanism in design-based inference. Second, we unify the analysis of crossover designs using least squares, with restrictions on the coefficients and weights on the units. Based on the theory, we recommend the specification of the regression function, weighting scheme, and coefficient restrictions to assess identifiability, construct efficient estimators, and estimate variances in a unified fashion. Crucially, the least squares procedure is simple to implement, and yields not only consistent and efficient point estimates but also valid variance estimates even when the working regression model is misspecified.