In principle the robustness approach (redoing the analysis with some different parameters) tries to show good faith. This matters because observational studies

In principle the robustness approach (redoing the analysis with some different parameters) tries to show good faith. This matters because observational studies have no meaningful replication, no second collection of data. In practice it just means practitioners gravitate towards methods more likely to show false positives. Like, this discontinuity at zero approach seems geared to show large effects from noise. When the linear regression across the dataset is a flat line, splitting at the discontinuity pretty much mathematically requires _any_ slope have an opposite slope on the other side.