ABSTRACT

The lack of adequate measures is often an impediment to robust policy evaluation. We discuss three approaches to measurement and data usage that have the potential to improve the way we conduct impact evaluations. First, the creation of new measures, when no adequate ones are available. Second, the use of multiple measures when a single one is not appropriate. And third, the use of machine learning algorithms to evaluate and understand programme impacts. We motivate the relevance of each of the categories by providing examples where they have proved useful in the past. We discuss the challenges and risks involved in each strategy and conclude with an outline of promising directions for future work.

摘要

缺乏适当的措施通常会阻碍强有力的政策评估。我们讨论了三种测量和数据使用方法，它们有可能改善我们进行影响评估的方式。首先，如果没有足够的措施，则要制定新的措施。其次，在一项措施不合适的情况下使用多种措施。第三，使用机器学习算法来评估和理解程序影响。我们通过提供过去证明有用的示例来激发每个类别的相关性。我们讨论了每种策略所涉及的挑战和风险，并总结了未来工作的有希望的方向。