  We study the problem of deriving policies, or rules, that when enacted on a
complex system, cause a desired outcome. Absent the ability to perform
controlled experiments, such rules have to be inferred from past observations
of the system's behaviour. This is a challenging problem for two reasons:
First, observational effects are often unrepresentative of the underlying
causal effect because they are skewed by the presence of confounding factors.
Second, naive empirical estimations of a rule's effect have a high variance,
and, hence, their maximisation can lead to random results.
  To address these issues, first we measure the causal effect of a rule from
observational data---adjusting for the effect of potential confounders.
Importantly, we provide a graphical criteria under which causal rule discovery
is possible. Moreover, to discover reliable causal rules from a sample, we
propose a conservative and consistent estimator of the causal effect, and
derive an efficient and exact algorithm that maximises the estimator. On
synthetic data, the proposed estimator converges faster to the ground truth
than the naive estimator and recovers relevant causal rules even at small
sample sizes. Extensive experiments on a variety of real-world datasets show
that the proposed algorithm is efficient and discovers meaningful rules.
