Make Point-in-Time Correctness the First Gate
Explain why point-in-time joins protect training data from future leakage.
A training dataset is a replay of a decision moment, not a warehouse export. The Time Anchor Every row used for model training needs a prediction time. That timestamp is the boundary between usable evidence and future leakage. A point-in-time join takes the entity row, looks backward from that timestamp, and selects feature values that were valid within the feature's TTL or freshness rule. Why It Works The model learns from whatever the dataset exposes. If the dataset includes support outcomes, chargeback confirmations, or later account states that were unavailable when the original decision happened, offline performance becomes inflated. The…
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