Title: Causal inference under over-simplified longitudinal causal models
Abstract: Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, in practice, repeated measurements are not always available. Then, practitioners tend to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective here was to assess whether - and how - the causal effect identified under such misspecified causal models relates to true causal effects of interest. We focus on situations regarding the type of available data for exposures: when they correspond to (i) ``instantaneous'' levels measured at inclusion in the study or (ii) summary measures of their levels up to inclusion in the study. In each of these two situations, we derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as true longitudinal causal effects of interest, or some weighted averages thereof. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that inference based on either ``instantaneous'' levels or summary measures usually returns quantities that do not directly relate to any causal effect of interest and should be interpreted with caution. They raise the need for the availability of repeated measurements and/or the development of sensitivity analyses when such data is not available.
Publication Year: 2018
Publication Date: 2018-10-02
Language: en
Type: preprint
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Cited By Count: 1
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