https://hackerpaper.kaipfister.de/p/2210.02943/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=33151707urn:hackerpapers:mention:2210.02943:33151707Mon, 10 Oct 2022 14:16:27 GMT<p>&quot;...Analysts are often interested in the explanations for surprising or unexpected results produced by their queries. ...A major challenge that hinders the interpretation of such queries is confounding bias, which can lead to an unexpected correlation. We generate explanations in terms of a set of confounding variables that explain the unexpected correlation observed in a query... We propose to mine candidate confounding variables from external sources since, in many real-life scenarios, the explanations are not solely contained in the input data... ...We present an efficient algorithm that…</p><p><a href="https://hackerpaper.kaipfister.de/p/2210.02943/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.DBhttps://news.ycombinator.com/item?id=33151706urn:hackerpapers:submission:2210.02943:33151706Mon, 10 Oct 2022 14:16:27 GMT<p>On Explaining Confounding Bias</p><p></p><p>1 point; 1 discussion reply on HN. <a href="https://hackerpaper.kaipfister.de/p/2210.02943/">Paper and source history</a>. A submission is not an endorsement.</p>cs.DB