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

Longitudinal study designs pose unique challenges for causal reasoning. In this article, we address the definition of a causal estimand as one such challenge. Following an example from psychological research, we provide a non-technical introduction to different estimands in longitudinal settings, with a particular focus on joint effects of treatment strategies — employing the potential outcomes framework and directed acyclic graphs. Additionally, we provide R-code to simulate and estimate these effects. In longitudinal research, joint effects are central because they extend average treatment effects to repeated interventions, offering a practical measure of combined intervention effects over time.Besides explaining the concept of joint effects, we discuss their applicability to psychological research. We focus on their interpretation and whether they can realistically be identified in longitudinal observational studies in psychology. In this context, addressing unmeasured confounding is a crucial aspect of causal inference, yet it is insufficiently discussed in the psychological literature. To bridge this gap, we propose a class of research designs for psychological studies where treatment assignment is driven by observable covariates so that joint effects can be identified under more reasonable assumptions.