It’s pretty common for people just starting to read research to confuse correlation (when two things seem to change together) with causation (when one thing causes another).
Correlation can be a clue towards finding causation, but it is not the same thing. There are lots of reasons why two data points might be correlated without assuming that A causes B. Some examples include:
The two data points are both caused by the same thing. Maybe being induced is correlated with outcome A. Maybe induction causes outcome A, or maybe being 41+ weeks is the real driver behind both induction AND outcome A.
Maybe it’s the reverse, and B causes A! It’s possible that the outcome you’re looking at makes it hard for the body to trigger labor, thereby increasing the chances of induction.
Maybe it goes both ways and the relationship is too complex for a simple this-causes-that conclusion.
The two data points are cyclical, and in this study they happened to line up. Seasons, New Year’s Resolutions, menstrual cycles, circadian rhythms, etc. can all be factors.
and finally there is always pure coincidence. Sometimes things are totally random and there’s no causal connection or association of any kind. My favorite place to look for funny random coincidences that look correlated is Spurious Correlations.