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This paper addresses the challenge of estimating peer effects in dynamic networks by introducing the Dynamic Network Doubly Robust estimator (DynaNet-DR), which accounts for the evolving nature of interaction graphs. By employing a controlled contrast framework, the authors differentiate between own-treatment, peer-exposure, and network evolution effects, leading to more accurate causal inferences. The proposed method demonstrates favorable estimation accuracy in semi-synthetic benchmarks, highlighting its robustness in scenarios where traditional methods may falter.
Evolving interaction graphs can obscure peer effects, but DynaNet-DR reveals how to disentangle these complexities for more accurate causal estimates.
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, DynaNet-DR, which combines a temporally factorized propensity with normalized augmentation. Under consistency, summary sufficiency, sequential exchangeability, positivity, nuisance convergence, and weak dependence, its canonical estimator is consistent when either the outcome regression or the propensity estimator is consistent. The reported implementation adds representative-score prediction, fixed clipping, and finite-sample stabilization. Semi-synthetic benchmarks on fixed real temporal graph sequences show favorable estimation accuracy among methods targeting the full profile. These benchmarks assess summary-indexed contrasts rather than counterfactual edge generation, and the MathOverflow study is an observational illustration under the stated assumptions.
