Probabilistic Identity Resolution: Cross-Device Attribution via Bayesian Graph Networks

Traditional attribution collapses at the identity layer — the same customer appears as dozens of anonymous profiles across devices, browsers, and sessions. This paper presents a probabilistic identity resolution framework using Bayesian graph networks with cross-device matching, deterministic anchoring, and privacy-preserving hashing. The framework achieves sub-second resolution at scale with explicit uncertainty quantification, enabling accurate multi-touch attribution across fragmented customer journeys. Complementary to Robinson (2026a) hybrid attribution and Robinson (2026b) media mix modeling.