  Understanding causality should be a core requirement of any attempt to build
real impact through AI. Due to the inherent unobservability of counterfactuals,
large randomised trials (RCTs) are the standard for causal inference. But large
experiments are generically expensive, and randomisation carries its own costs,
e.g. when suboptimal decisions are trialed. Recent work has proposed more
sample-efficient alternatives to RCTs, but these are not adaptable to the
downstream application for which the causal effect is sought. In this work, we
develop a task-specific approach to experimental design and derive sampling
strategies customised to particular downstream applications. Across a range of
important tasks, real-world datasets, and sample sizes, our method outperforms
other benchmarks, e.g. requiring an order-of-magnitude less data to match RCT
performance on targeted marketing tasks.

Methodology;Machine Learning;Machine Learning