Towards a Clearer Understanding of Causal Estimands: The Importance of Joint Effects in Longitudinal Designs With Time-Varying Treatments

Longitudinal studies with time-varying treatments or exposures make it hard to figure out “what effect” is being estimated. Drawing on causal inference, we clarify this by distinguishing between total, direct, and—centrally—joint effects, defined within the potential-outcomes framework and illustrated with directed acyclic graphs. Joint effects extend average treatment effects to repeated interventions, providing a practical measure of combined intervention effects over time. Using a worked example on smartphone use and sleep quality, we demonstrate how different estimands answer different questions, why single total effects can sometimes mislead in longitudinal settings, and how joint effects capture strategy-level consequences across time. A key practical takeaway is that joint effects can be estimated in both experimental and observational studies. In the latter, it typically suffices to adjust only for variables that govern treatment decisions at each time point rather than modeling the entire causal system. Building on this, we propose covariate-driven treatment assignment (information-restriction designs in which decisions depend only on observed covariates) as a practical route to causal inference in nonexperimental psychology, and we connect these designs to estimation via g-methods from epidemiology. We provide open materials, including R code, to support adoption.