In this paper we discuss multi-agent causal models, which are an extension of causal Bayesian networks to the multi-agent case. In this paper we illustrate how these recently introduced models could prove useful for dependability analysis. Their main difference with other graphical modeling techniques that have been applied to the problem is that multi-agent causal models allow for multi-agent, privacy-preserving quantitative causal inference in models with hidden variables.