PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments

[2505.09094] PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Human-Computer Interaction arXiv:2505.09094 (cs) [Submitted on 14 May 2025 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments Authors: London Bielicke , Anna Zhang , Shruti Tyagi , Emery Berger , Adam Chlipala , Eunice Jun View a PDF of the paper titled PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments, by London Bielicke and 5 other authors View PDF HTML (experimental) Abstract: Experimental designs reflect assumptions about variable relationships that determine what causal queries researchers can answer through the experiment. Accounting for and communicating these assumptions is essential for drawing valid, generalizable conclusions from scientific experiments. Unfortunately, existing experimental design tools elide these details, expecting researchers to reason about design decisions and assumptions on their own. To surface assumptions and enable design exploration, we introduce a grammar of composable operators for constructing experimental assignment procedures grounded in matrix algebra. The PLanet DSL implements this grammar and compiles PLanet programs into constraint satisfaction problems over matrices. Together, PLanet's composable grammar and matrix representation enable a static analysis to determine which causal queries are testable under different assumptions. In an expressivity evaluation, PLanet was the most expressive of existing DSLs. Critical reflections with the authors of these DSLs revealed that PLanet makes design choices explicit without requiring procedural specification. Think-aloud studies showed that PLanet facilitated design exploration and surfaced assumptions researchers may otherwise overlook. Comments: 10 pages Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2505.09094 [cs.HC] (or arXiv:2505.09094v3 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2505.09094 Focus to learn more arXiv-issued DOI via DataCite Submission history From: London Bielicke [ view email ] [v1] Wed, 14 May 2025 02:57:09 UTC (490 KB) [v2] Fri, 12 Sep 2025 22:47:53 UTC (844 KB) [v3] Tue, 14 Apr 2026 11:19:17 UTC (2,541 KB) Full-text links: Access Paper: View a PDF of the paper titled PLanet: Formalizing and Analyzing Assignment Procedures in the Design of Experiments, by London Bielicke and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2025-05 Change to browse by: cs 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

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