{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discovering-causal-signals-in-images","title":"Discovering Causal Signals in Images","arxiv_id":"1605.08179","date":"2016-05-26","proceeding":"CVPR 2017 7","authors":["David Lopez-Paz","Robert Nishihara","Soumith Chintala","Bernhard Schölkopf","Léon Bottou"],"abstract":"This paper establishes the existence of observable footprints that reveal the\n\"causal dispositions\" of the object categories appearing in collections of\nimages. We achieve this goal in two steps. First, we take a learning approach\nto observational causal discovery, and build a classifier that achieves\nstate-of-the-art performance on finding the causal direction between pairs of\nrandom variables, given samples from their joint distribution. Second, we use\nour causal direction classifier to effectively distinguish between features of\nobjects and features of their contexts in collections of static images. Our\nexperiments demonstrate the existence of a relation between the direction of\ncausality and the difference between objects and their contexts, and by the\nsame token, the existence of observable signals that reveal the causal\ndispositions of objects.","url_abs":"http://arxiv.org/abs/1605.08179v2","url_pdf":"http://arxiv.org/pdf/1605.08179v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discovering-causal-signals-in-images","repo_url":"https://github.com/euphoria0-0/Neural-Causation-Coefficient","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"discovering-causal-signals-in-images","repo_url":"https://github.com/kyrs/NCC-experiments","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1605.08179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.08179"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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