{"url":"/dataset/jobs","name":"Jobs","full_name":null,"description_markdown":"The Jobs dataset by LaLonde [36] is a widely used benchmark in the causal inference community, where the treatment is job training and the outcomes are income and employment status after training. The dataset includes 8 covariates such as age, education, and previous earnings. Our goal is to predict unemployment, using the feature set of Dehejia and Wahba [37]. Following Shalit et al. [8], we combined the LaLonde experimental sample (297 treated, 425 control) with the PSID comparison group (2490 control).","description_withheld":null,"homepage":"","introduced_date":"2016-06-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/estimating-individual-treatment-effect","title":"Estimating individual treatment effect: generalization bounds and algorithms","first_author":"Uri Shalit","url":null},"license":null,"modalities":[],"tasks":[{"name":"Causal Inference","url":"/task/causal-inference","datasets_with_task":"/datasets/task/causal-inference"}],"languages":[],"variants":["Jobs"],"data_loaders":[{"repo":"https://gitlab.com/aiprojects4133548/jobsuitabilityproject","url":"https://gitlab.com/aiprojects4133548/jobsuitabilityproject","frameworks":["tf"]}],"num_papers_in_archive":56,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/causal-inference-on-jobs","task":"Causal Inference","dataset_variant":"Jobs","rows":5,"metrics":["Average Treatment Effect on the Treated Error"],"first_row_in_archive_order":{"model":"BCAUSS","paper":"/paper/learning-end-to-end-patient-representations","metrics":{"Average Treatment Effect on the Treated Error":"0.05"},"code_links":[{"title":"anthem-ai/bcauss","url":"https://github.com/anthem-ai/bcauss"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-end-to-end-patient-representations","title":"Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation","date":"2023-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ganite-estimation-of-individualized-treatment","title":"GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets","date":"2018-01-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/estimating-individual-treatment-effect","title":"Estimating individual treatment effect: generalization bounds and algorithms","date":"2016-06-13","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bart-bayesian-additive-regression-trees","title":"BART: Bayesian additive regression trees","date":"2008-06-19","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}