论文标题
实现：现实的因果推理基准测试
RealCause: Realistic Causal Inference Benchmarking
论文作者
论文摘要
因果推断中有许多不同的因果效应估计量。但是，目前尚不清楚如何在这些估计器之间进行选择，因为没有因果关系的基础真相。一个常用的选项是模拟合成数据，在该数据中已知。但是，合成数据的最佳因果估计量不太可能是实际数据的最佳因果估计量。因果估计量的理想基准是（a）因果效应的产量基地值，以及（b）代表真实数据。使用柔性生成模型，我们提供了一个基准，该基准既可以产生地面真相，又是现实的。使用此基准测试，我们评估了1500多种不同的因果估计量，并提供了证据表明，使用预测指标选择因果估计量的超参数是合理的。
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 real 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 over 1500 different causal estimators and provide evidence that it is rational to choose hyperparameters for causal estimators using predictive metrics.
