[2111.05243] Bounding Treatment Effects by Pooling Limited Information across Observations Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2111.05243 (econ) [Submitted on 9 Nov 2021 ( v1 ), last revised 9 May 2026 (this version, v8)] Title: Bounding Treatment Effects by Pooling Limited Information across Observations Authors: Sokbae Lee , Martin Weidner View a PDF of the paper titled Bounding Treatment Effects by Pooling Limited Information across Observations, by Sokbae Lee and 1 other authors View PDF HTML (experimental) Abstract: We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through two empirical application that our bounds are indeed robust and informative in practice. Subjects: Econometrics (econ.EM) ; Methodology (stat.ME) Cite as: arXiv:2111.05243 [econ.EM] (or arXiv:2111.05243v8 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2111.05243 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Martin Weidner [ view email ] [v1] Tue, 9 Nov 2021 16:27:25 UTC (61 KB) [v2] Mon, 13 Dec 2021 15:54:48 UTC (63 KB) [v3] Wed, 29 Nov 2023 10:53:48 UTC (93 KB) [v4] Tue, 12 Dec 2023 11:18:32 UTC (736 KB) [v5] Mon, 5 May 2025 11:06:42 UTC (60 KB) [v6] Mon, 9 Feb 2026 16:57:22 UTC (67 KB) [v7] Fri, 24 Apr 2026 15:56:56 UTC (67 KB) [v8] Sat, 9 May 2026 11:14:36 UTC (63 KB) Full-text links: Access Paper: View a PDF of the paper titled Bounding Treatment Effects by Pooling Limited Information across Observations, by Sokbae Lee and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2021-11 Change to browse by: econ stat stat.ME References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from