When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects
Read the original on arXiv Machine Learning →The paper introduces a controlled contrast framework for estimating peer effects when interaction graphs evolve, indexing potential outcomes by own treatment, temporally aggregated peer exposure, and a post‑assignment evolution summary. It proposes the Dynamic Network Doubly Robust estimator (DynaNet‑DR), which uses a temporally factorized propensity and normalized augmentation to achieve consistency under standard causal assumptions. Semi‑synthetic benchmarks on real temporal graph sequences demonstrate that DynaNet‑DR achieves favorable estimation accuracy compared to other methods, and an observational study on MathOverflow illustrates its practical application.
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