Causal definitions versus casual estimation: Reply to Valente et al. (2022).

In this response to Valente et al. (2022), I am discussing the plausibility and applicability of the proposed mediation model and its causal effects estimation for single case experimental designs (SCEDs). I will focus on the underlying assumptions that the authors use to identify the causal effects. These assumptions include the particularly problematic assumption of sequential ignorability or no-unmeasured confounders. First, I will discuss the plausibility of the assumption in general and then particularly for SCEDs by providing an analytic argument and a reanalysis of the empirical example in Valente et al. (2022). Second, I will provide a simulation that reproduces the design by Valente et al. (2022) with the exception that, for a more realistic depiction of empirical data, an unmeasured confounder affects the mediator and outcome variables. The results of this simulation study indicate that even minor violations will lead to Type I error rates up to 100% and coverage rates as low as 0% for the defined causal direct and indirect effects. Third, using historical data on the effect of birth control on stork population and birth rates, I will show that mediation models like the proposed method can lead to surprising artifacts. These artifacts can hardly be identified with statistically means including methods such as sensitivity analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).