A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
Causal inference under network interference using a mixture of randomized experiments
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SphUnc decomposes uncertainty via hyperspherical von Mises-Fisher latents and performs causal identification through structural models on those latents.
citing papers explorer
-
Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
A robust design selector minimizes worst-case planning risk over an ambiguity set of exposure mechanisms, with Wasserstein bounds and selector theorems, yielding different recommendations on public datasets.
-
SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
SphUnc decomposes uncertainty via hyperspherical von Mises-Fisher latents and performs causal identification through structural models on those latents.