Outcome-wide longitudinal designs for causal inference: a new template for empirical studies

TL;DR

This paper introduces the outcome-wide longitudinal design, a new empirical template for assessing causal effects across multiple outcomes, enhancing robustness, efficiency, and policy relevance in causal inference studies.

Contribution

It proposes a comprehensive framework for outcome-wide longitudinal studies, extending traditional causal analysis to multiple outcomes with improved control and evaluation methods.

Findings

Advantages over traditional single-outcome studies

Enhanced robustness and sensitivity analysis methods

Increased efficiency and policy relevance

Abstract

In this paper we propose a new template for empirical studies intended to assess causal effects: the outcome-wide longitudinal design. The approach is an extension of what is often done to assess the causal effects of a treatment or exposure using confounding control, but now, over numerous outcomes. We discuss the temporal and confounding control principles for such outcome-wide studies, metrics to evaluate robustness or sensitivity to potential unmeasured confounding for each outcome, and approaches to handle multiple testing. We argue that the outcome-wide longitudinal design has numerous advantages over more traditional studies of single exposure-outcome relationships including results that are less subject to investigator bias, greater potential to report null effects, greater capacity to compare effect sizes, a tremendous gain in the efficiency for the research community, a greater policy relevance, and a more rapid advancement of knowledge. We discuss both the practical and theoretical justification for the outcome-wide longitudinal design and also the pragmatic details of its implementation.