AI RESEARCH
Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
arXiv CS.LG
•
ArXi:2603.11942v1 Announce Type: new Synthetic Nearest Neighbors (SNN) provides a principled solution to causal matrix completion under missing-not-at-random (MNAR) by exploiting local low-rank structure through fully observed anchor submatrices. However, its effectiveness critically relies on sufficient data availability within each treatment level, a condition that often fails in settings with multiple or complex treatments.