{"url":"/dataset/acre","name":"ACRE","full_name":"Abstract Causal REasoning","description_markdown":"Abstract Causal REasoning (ACRE) is a dataset for the systematic evaluation of current vision systems in causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables.\r\n\r\nEach split of the dataset is structured as follows:\r\n\r\n```\r\nconfig/\r\n    train.json\r\n    val.json\r\n    test.json\r\nimages/\r\n    ACRE_train_00*.png\r\n    ACRE_val_00*.png\r\n    ACRE_test_00*.png\r\nscenes/\r\n    ACRE_train_00*.json\r\n    ACRE_val_00*.json\r\n    ACRE_test_00*.json\r\n```\r\nEach image file in the images folder has a corresponding scene description file in scenes with the same name (except for the extension).\r\n\r\nEach ACRE problem is named after `ACRE_{train/val/test}_{6_digit_problem_idx}_{2_digit_panel_idx}`","description_withheld":null,"homepage":"http://wellyzhang.github.io/project/acre.html","introduced_date":"2021-03-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/acre-abstract-causal-reasoning-beyond","title":"ACRE: Abstract Causal REasoning Beyond Covariation","first_author":"Chi Zhang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[],"variants":["ACRE"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}