We propose that causal attribution involves constructing a coherent story using mechanism information (i.e., the processes underlying the relationship between the cause and the effect). This processing account can explain both the conjunction effect (i.e., conjunctive explanations being rated more probable than their components) and the discounting effect (i.e., the effect of one cause being discounted when another cause is already known to be true). In the current experiment, both effects occurred with mechanism-based explanations but not with covariation-based explanations in which the cause-effect relationship was phrased in terms of covariations without referring to mechanisms. We discuss why the current results pose difficulties for previous attribution models in Psychology and Artificial Intelligence.