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September 5, 2025Open Access

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

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Causal estimands are crucial for understanding joint effects in psychological research over time, as shown in examples.
The study highlights how joint effects extend average treatment effects, particularly in longitudinal designs with time-varying treatments.
Observational studies must address unmeasured confounding to improve causal inference, indicating significant methodological challenges.
Proposed research designs focus on using observable covariates for treatment assignment, enhancing the identification of joint effects.

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Longitudinal designs address joint effects of time-varying treatments in psychological research, suggesting implications for causal inference.

Junker et al. (2025) studied this question.

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Longitudinal designs address joint effects of time-varying treatments in psychological research, suggesting implications for causal inference.