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.

通过任务特定的实验设计和推导到定制特定下游应用程序的采样策略，我们提出了一种更节省样本数据的替代随机对照试验的方法，用于因果推断，并在各种重要任务，实际数据集和样本大小的情况下，胜过其他基准，例如需要一种数量级的数据才能在定向营销任务中达到随机对照试验的性能。

针对任务的治疗效果估计实验设计