There are many different causal effect estimators in causal inference. However, it is unclear how to choose between these estimators because there is no ground-truth for causal effects. A commonly used option is to simulate synthetic data, where the ground-truth is known. However, the best causal estimators on synthetic data are unlikely to be the best causal estimators on realistic data. An ideal benchmark for causal estimators would both (a) yield ground-truth values of the causal effects and (b) be representative of real data. Using flexible generative models, we provide a benchmark that both yields ground-truth and is realistic. Using this benchmark, we evaluate 66 different causal estimators.

翻译：在因果推断中有许多不同的因果估计因素,但不清楚如何在这些估计因素之间作出选择,因为没有因果关系的地面真实性。通常使用的一种选择是模拟合成数据,因为地面真实性已经为人所知。然而,合成数据的最佳因果估计因素不大可能是现实数据的最佳因果估计因素。因果估计因素的理想基准将(a) 产生因果结果的地面真实性值,(b) 代表真实数据。我们使用灵活的基因模型提供了一个基准,既能产生地面真实性,又现实性。我们使用这一基准评估了66个不同的因果估计因素。