For the case with a single causal variable, David et al. (2014) defined the probability of causation and Pearl (2000) defined the probability of necessity to assess the causes of effects. For a case with multiple causes which may affect each other, this paper defines the posterior total and direct causal effects based on the evidences observed for post-treatment variables, which could be viewed as measurements of causes of effects. The posterior causal effects involve the probabilities of counterfactual variables. Thus, like probability of causation, probability of necessity and the direct causal effects, the identifiability of the posterior total and direct causal effects requires more assumptions than the identifiability of the traditional causal effects conditional on pre-treatment variables. We present assumptions required for the identifiability of the posterior causal effects and provide identification equations. Further, when the causal relationships among multiple causes and an endpoint may be depicted by causal networks, we can simplify both the required assumptions and the identification equations of the posterior total and direct causal effects. Finally, using numerical examples, we compare the posterior total and direct causal effects with other measures for evaluating the causes of effects and the population attributable risks.