  Lifted inference exploits symmetries in probabilistic graphical models by
using a representative for indistinguishable objects, thereby speeding up query
answering while maintaining exact answers. Even though lifting is a
well-established technique for the task of probabilistic inference in
relational domains, it has not yet been applied to the task of causal
inference. In this paper, we show how lifting can be applied to efficiently
compute causal effects in relational domains. More specifically, we introduce
parametric causal factor graphs as an extension of parametric factor graphs
incorporating causal knowledge and give a formal semantics of interventions
therein. We further present the lifted causal inference algorithm to compute
causal effects on a lifted level, thereby drastically speeding up causal
inference compared to propositional inference, e.g., in causal Bayesian
networks. In our empirical evaluation, we demonstrate the effectiveness of our
approach.
