Abstract Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is calibrate the biased sample benchmark by adjusting subject weights. However, hard calibration can produce enormous weights when an exact enforced on a large set of extraneous covariates. This article proposes soft scheme, where outcome i...