The Unfulfilled Promise of Longitudinal Designs for Causal Inference

Longitudinal designs are frequently used in psychological research. An intuitive analytic approach is to adjust for previous measurements to bolster the validity of causal conclusions when estimating the effect of a focal predictor (i.e., treatment) on an outcome. This approach is routinely applied but rarely substantiated in practice. What are the implications of adjusting for previous measurements? Does it necessarily improve causal inferences? In this paper, we demonstrate that answers to these questions are far from straightforward. We explain how adjusting for previous measurements can reduce or induce bias in common longitudinal scenarios. We further demonstrate, in scenarios with less stringent causal assumptions, adjusting or not adjusting for previous measurements can induce bias one way or the other. Put differently, adjusting or not adjusting for a previous measurement can simultaneously strengthen and undermine causal inferences from longitudinal research, even in the simplest scenarios. We urge researchers to overcome the unwarranted complacency brought on by using longitudinal designs to test causality. Practical recommendations for strengthening causal conclusions in psychology research are provided.