论文标题
单个治疗效果的共形预测间隔
Conformal prediction intervals for the individual treatment effect
论文作者
论文摘要
我们在非参数回归设置中提出了几个针对单个治疗效果的预测间隔程序，其中有限样本或渐近覆盖范围保证，在这种情况下，允许非线性回归函数，异性疾病和非高斯性。构造的预测间隔我们使用Vovk等人的共形方法。 （2005）。在广泛的模拟中，我们比较了预测间隔程序的覆盖率概率和间隔长度。我们证明，如果样本量足够大，那么复杂的学习算法（例如神经网络）可以比简单的算法（例如线性回归）导致预测间隔狭窄。
We propose several prediction intervals procedures for the individual treatment effect with either finite-sample or asymptotic coverage guarantee in a non-parametric regression setting, where non-linear regression functions, heteroskedasticity and non-Gaussianity are allowed. The construct the prediction intervals we use the conformal method of Vovk et al. (2005). In extensive simulations, we compare the coverage probability and interval length of our prediction interval procedures. We demonstrate that complex learning algorithms, such as neural networks, can lead to narrower prediction intervals than simple algorithms, such as linear regression, if the sample size is large enough.
