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

Longitudinal study designs present unique challenges for causal reasoning. In this article, weaddress the precise definition of a causal estimand as one of these challenges. Using examplesfrom psychological research, we provide a non-technical introduction to estimands andillustrate three broader classes of estimands—total, direct, and joint effects—using thepotential outcomes framework and directed acyclic graphs. In longitudinal research, jointeffects play a central role because they extend average treatment effects to repeatedtreatments and thus provide a practical measure of cumulative intervention effects over time.Besides explaining the concept of joint effects and how they relate to mediation, wediscuss their applicability to psychological research. We focus on their interpretation andwhether they can realistically be identified in longitudinal observational studies inpsychology. In this context, addressing unmeasured confounding is a crucial aspect of causalinference and mediation analyses, yet it is insufficiently discussed in the psychologicalliterature. To bridge this gap, we propose a class of research designs for psychological studieswhere treatment assignment is driven by observable covariates so that joint effects can beidentified under more reasonable assumptions.