Soft calibration for selection bias problems under mixed-effects models

Understand

Calibration weighting has been widely used to correct selection biases in non-probability sampling, missing data, and causal inference.

• The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights.
• However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates.
• This article proposes a soft calibration scheme, in which the outcome and the selection indicator follow mixed-effects models.