Calibration Error for Heterogeneous Treatment Effects

Understand

Recently, many researchers have advanced data-driven methods for modeling heterogeneous treatment effects (HTEs).

• Even still, estimation of HTEs is a difficult task -- these methods frequently over- or under-estimate the treatment effects, leading to poor calibration of the resulting models.
• However, while many methods exist for evaluating the calibration of prediction and classification models, formal approaches to assess the calibration of HTE models are limited to the calibration slope.
• In this paper, we define an analogue of the \smash{($\ell_2$)} expected calibration error for HTEs, and propose a robust estimator.