Dynamic Treatment on Networks
In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at a well-connected node can trigger cascades that change which nodes are worth targeting in the next period. Existing treatment strategies under network interference are largely static while dynamic treatment frameworks typically ignore network structure altogether. We integrate these perspectives and propose Q-Ising, a three-stage pipeline that (i) estimates network adoption dynamics via a Bayesian dynamic Ising model from a single observed panel, (ii) augments treatment adoption histories with continuous posteri...
| Authors | Bengusu Nar, Jiguang Li, Veronika Ročková et al. |
|---|---|
| Primary category | stat.ML |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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