= Peter C. Austin
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<Peter C. Austin> develops methods for <observational studies>, including <propensity score> matching. In his simulation study of <caliper matching>, treated and untreated people are paired only when their scores are sufficiently close.<Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies by Peter C. Austin>[Original byline and simulation design]{ref}
The caliper creates a practical tradeoff: a wider tolerance can retain more pairs but allow larger differences in measured baseline risk; a tighter tolerance can discard people and change the population represented by the comparison. Austin compared resulting <bias>, <mean squared error> and <confidence interval> coverage under specified data-generating scenarios. His numerical recommendations concern those estimands and scenarios, rather than a guarantee that matching removes unmeasured <confounding>.<Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies by Peter C. Austin>[Abstract, continuous and binary outcomes and simulation performance measures]{ref}
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