# Intuition behind Brier score weighing step for censored data

Sources seem to suggest that when calculating Brier scores involving right-censored data, one must weigh the otherwise mean square error function with the inverse probability of censoring weights method (via Kaplan–Meier estimator). (Example: https://square.github.io/pysurvival/metrics/brier_score.html)

What is the intuition behind such a weighing step? What happens if I skip this step?

... we can think of censoring $$C$$ as just another treatment. That is, the goal of the analysis is to compute the causal effect of a joint intervention on [treatment] $$A$$ and $$C$$. To eliminate selection bias for the effect of treatment $$A$$, we need to adjust for confounding for the effect of treatment $$C$$.