Proximity is measured over citations between two papers we both hold, weighted by how heavily one leans on the other, and is symmetric — it does not distinguish citing from being cited. Authors without a profile here are skipped, and a genuinely close colleague can be missing simply because their work is not in our arXiv corpus. Method: docs/06-citations-pipeline.md.
Papers
(1 of 7)
Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis
published2026 · Proceedings of the AAAI Conference on Artificial Intelligence · first circulated 2025
Conceptualizing Treatment Leakage in Text-based Causal Inference
published2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 5 citations
Assembled from arXiv and OpenAlex. Duplicate records for the same paper are merged, and the published version is shown where we could identify one. Corrections welcome.