arxiv
PublishedOctober 22, 2026 at 4:00 AM
Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding
Publisher summary· verbatim
arXiv:2609.40051v2 Announce Type: replace-cross Abstract: Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounder
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivRetrieval Augmented Generation Compositionality10harxivEvolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting10harxivTowards English - Mizo Legal Machine Translation: Corpus Creation, Analysis, and Benchmarks10harxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents10hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗