Question

What assumptions are necessary for causal inference in a cross-sectional regression analysis?

Answer 1

At the outset, five conditions are necessary for a cross-sectional approach to be able to investigate an etiological hypothesis, and without which any attempt to relate an ensuing estimator to either the CIR or IDR breaks down. For one, the population must be in steady state over the study period (stationary). In this case, within any given period of time, the size of the population needs to be constant across the exposure groups, as well as in regards to any other co-variable used in the modeling process. Secondly, no selective survival is allowable, i.e., the probability of withdrawal or death from the outcome under study or from other related causes may not be different across exposure groups. Thirdly, the mean duration of the outcome must be the same regardless of exposure group, that is, the exposure may not differentially influence the survival or recovery probabilities. Fourthly, no reverse causality is allowed, i.e., the outcome being modeled may not reciprocally cause (influence) the exposure status in any way. Lastly, the temporal directionality from the exposure to the outcome must be sustainable, either theoretically (e.g., if a lifelong attribute is studied as the exposure for a recent outcome event) or by means of a thorough data collection procedure that assures the exposure as an antecedent of the outcome (e.g., in a study on the effects on child birth, recalling at birth a past exposure during pregnancy)