| Abstract |
Discovering causal relations from data represents the core of the scientific method. In most cases the causal relations are recovered from experimental data in which the variable of interest is perturbed, but seminal work from Spirtes and Pearl demonstrates that, under certain assumptions, it is already possible to exclude several implausible causal models of the data by using only observational data. Constraint-based causal discovery methods use statistical (in)dependences from the data to express constraints over all the possible causal models. One of the most promising formulations of this problem is in logic, which allows for quick prototyping, combination of algorithms and an easy integration of complex background knowledge. On the other side, a purely logic approach cannot handle noise in the (in)dependence test results, making the case for the use of probabilistic logics. In this talk I will present two algorithms for probabilistic logical causal inference that we have been developing with Joris Mooij from UvA and Tom Claassen from RU Nijmegen. Compared to other existing methods, our algorithms are more scalable and simpler to encode, while preserving a comparable accuracy in the prediction of indirect causal and acausal relationships. |