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.

中文翻译：

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RealCause：现实的因果推断基准

因果推断中有许多不同的因果效应估计量。但是，尚不清楚如何在这些估计量之间进行选择，因为没有因果关系的事实。常用的方法是模拟已知地面真相的合成数据。但是，综合数据上的最佳因果估计量不太可能是现实数据上的最佳因果估计量。因果估计量的理想基准既可以（a）得出因果效应的真实值，又可以（b）代表真实数据。使用灵活的生成模型，我们提供了既能产生真实性又是现实的基准。使用该基准，我们评估了66种不同的因果估计量。