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Many epidemiological studies seek to assess the effect of one or several exposures on one or more outcomes. However, to quantify the causal inference produced, statistical techniques are commonly used that contrast the association among the variables of interest, not precisely of causal effect.(1) In fact, although these measures may not have a causal interpretation, the results are often adjusted for all potential confounding factors. (2,3) Some contemporary epidemiologists developed new methodological tools for causal inference,like the theory or contra-factual model(4) and representation of causal effects through the Directed Acyclic Graph (DAG).(5) The DAG, a fusion of the probability theory with trajectory diagrams, is quite useful to visually deduct the statistical associations implied by the causal relations among the study variables.
