SOURCE-LINKED INTELLIGENCE
When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects
Peer effects are difficult to estimate when interaction graphs evolve because pre-assignment network history, dynamic peer exposure, and post-assignment network change have distinct causal roles. We introduce a controlled contrast framework that indexes potential outcomes by own treatment, temporally aggregated peer exposure, and a post-assignment evolution summary. Differences between the resulting means define own-treatment, peer-exposure, controlled network-evolution, and joint controlled contrasts rather than a mediation decomposition. We develop the Dynamic Network Doubly Robust estimator
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T14:32:40.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.