English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Evaluating novel contextual bandit policies using logged data is crucial inapplications where exploration is costly, such as medicine. But it usuallyrelies on the assumption of no unobserved confounders, which is bound to failin practice. We study the question of policy evaluation when we instead haveproxies for the latent confounders and develop an importance weighting methodthat avoids fitting a latent outcome regression model. We show that unlike theunconfounded case no single set of weights can give unbiased evaluation for alloutcome models, yet we propose a new algorithm that can still provablyguarantee consistency by instead minimizing an adversarial balance objective.We further develop tractable algorithms for optimizing this objective anddemonstrate empirically the power of our method when confounders are latent.

Policy Evaluation with Latent Confounders via Optimal Balance