Release v0.6.1: Optimal Full Matching and mahvars Caliper Fix

What's New in v0.6.1

This release fixes two matching bugs. Results change for full matching, and for nearest neighbor matching that combines mahvars with a caliper; other methods are unaffected.

Changed results

• Full matching is now optimal: it finds the subclasses with the smallest total distance between treated and control units, as R's optmatch does. A subclass is one treated unit with one or more controls, or one control with one or more treated units. Before, every unit of the smaller group was first paired with a unit of its own, which forced distant pairs when the groups overlap little: on the Lalonde data the standardized mean difference of black after matching was 1.02 and is now 0.02.
• min_controls_per_subclass and max_controls_per_subclass are part of the optimization instead of being applied afterwards. Controls that max_controls_per_subclass leaves no room for stay unmatched, as few as possible. If there are too few controls to give every treated unit min_controls_per_subclass of its own, subclasses are merged, now with a warning.
• Nearest neighbor matching with mahvars and a caliper applies the caliper to the propensity score. It was compared with the Mahalanobis distance instead, which left most units unmatched and allowed pairs that were far apart on the propensity score. Optimal, full and genetic matching were not affected.

Fixed

• A caliper on the distance measure with distance="mahalanobis" (which estimates no propensity score), and invalid min_controls_per_subclass or max_controls_per_subclass values, raise an error that says what to change.