  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.
