Knowledge in the form “A causes B” and “A does not cause B” is often available. For example, in an experiment where temperature (A) is controlled, then for any quantity B that changes we can infer A causes B. Our new publication in ICML shows how to incorporate such knowledge when inducing a causal models from other datasets.