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.

该研究论文使用灵活的生成模型提供基准，既可以提供因果效应的真实值，又可以代表真实数据，评估了 1500 多种不同的因果效应估计器，并提供证据表明使用预测指标选择因果效应估计器的超参数是合理的。

RealCause: 真实因果推断基准测试

RealCause: Realistic Causal Inference Benchmarking

Continuous treatments (e.g., doses) arise often in practice, but many
available causal effect estimators are limited by either requiring parametric
models for the effect curve, or by not allowing doubly robust covariate
adjustment. We develop a novel kernel smoothing approach that requires only
mild smoothness assumptions on the effect curve, and still allows for
misspecification of either the treatment density or outcome regression. We
derive asymptotic properties and give a procedure for data-driven bandwidth
selection. The methods are illustrated via simulation and in a study of the
effect of nurse staffing on hospital readmissions penalties.

本文提出一种新的核平滑方法，可以在不需要参数模型的情况下，对连续治疗（如剂量）进行因果效应估计，并允许处理密度或结果回归的错误规范。方法经过了模拟证明，并应用于护士人员配备对医院复诊惩罚的研究中。