Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
The study investigates whether real-world datasets contain natural experiments, which are implicit interventions affecting certain groups. The authors utilize causal discovery to analyze datasets and determine if treating them as interventional improves model performance. Their findings suggest that real-world datasets do indeed contain natural experiments, offering a new avenue for enhancing causal inference in data analysis.
- ▪Natural experiments are events that affect some individuals or groups but not others, serving as implicit interventions.
- ▪The study employs causal discovery to recover causal graphs and perform feature selection based on causal links.
- ▪Results indicate that real-world datasets contain natural experiments, which can improve model performance when treated as interventional.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03251 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | fudwQZv60tbn |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2606.03251 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 2 Jun 2026] Title:Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection Authors:Gautam Gare, John Galeotti, Michael Mozer, Deva Ramanan, Nan Rosemary Ke View a PDF of the paper titled Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection, by Gautam Gare and 4 other authors View PDF Abstract:In nature, events that…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.