Q-Ising integrates Bayesian dynamic Ising modeling with offline RL to enable adaptive network treatment policies that outperform static centrality benchmarks under spillovers.
arXiv preprint arXiv:2507.00312 , year=
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Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.
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Dynamic Treatment on Networks
Q-Ising integrates Bayesian dynamic Ising modeling with offline RL to enable adaptive network treatment policies that outperform static centrality benchmarks under spillovers.
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Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.