arXiv Machine Learning By Ezinne Nwankwo, Lauri Goldkind, Angela Zhou

Optimal Causal Annotations: An Application to Casenotes in Social Services

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arXiv:2502. 10605v4 Announce Type: replace-cross Abstract: Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain.

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arXiv Machine Learning
Sep 23

Optimal Sequential Annotations for Off-Policy Evaluation

The paper proposes a method for allocating a limited budget of expert annotations to optimize the accuracy of off-policy evaluation in settings where rewards are missing or noisy. By deriving variance‑optimal annotation probabilities for sequential, forward‑monotone protocols, the authors provide a batch‑adaptive implementation that can be applied to real data. Experiments on casenotes from a homelessness services nonprofit and on human‑preference votes from LMArena demonstrate substantial reductions in RMSE—up to 65% for housing placement and 68% for progress toward a housing application—when using only 40% or more of the full annotation budget.

By Woojin Chae, Ezinne Nwankwo, Haitong Qin, Angela Zhou