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Part II includes drug abuse violations, simple assault, fraud/forgery, stolen property, vandalism, weapons violations, prostitution, disorderly conduct, and other non\u2011index offenses. Computed as Part II arrests divided by all arrests reported for that year. |\n| HASCOMPSTAT | Binary indicator (0/1) that the police department operated a CompStat\u2011style performance/metrics management system in that year. \u201cCompStat\u2011style\u201d covers systems that regularly compile incident/arrest statistics, visualize patterns, and use management meetings for accountability once implemented and in subsequent years. |\n| AGENCY | Text identifier for the municipal police department/jurisdiction (e.g., \u201cPHOENIX\u201d). Serves as the unit index across years and corresponds to the primary city police agency reporting to the UCR program. |\n| YEAR | Calendar year of observation, aligning with UCR annual reporting (1990\u20132013). |\n| PART2arrests | Annual count of arrests recorded for UCR Part II offenses by the department. Includes adult and juvenile arrests as submitted to the UCR program. Integer count. |\n| POP | Resident population of the city for that year, based on UCR population figures (census-based estimates used by UCR for rate calculations). |\n\nDataset Context:\nThis file is a municipal police department\u2013by\u2013year panel for large U.S. cities covering 1990\u20132013. It merges administrative statistics from the FBI\u2019s Uniform Crime Reporting (UCR) program with hand-collected information on whether and when departments used CompStat\u2011style metrics management. UCR provides standardized definitions, monthly submissions from local agencies, and annual tabulations of arrests and population. Part II arrests span a wide set of non\u2011index offenses\u2014such as drug violations, simple assault, property and public\u2011order offenses\u2014that are often more discretionary than index (Part I) crimes. The population series comes from UCR\u2019s census\u2011based annual estimates and is commonly used to construct per\u2011capita measures.\n\nCompStat\u2011style systems began spreading among U.S. police departments in the mid\u20111990s alongside advances in data systems and the broader rise of performance management in public administration. These systems typically feature frequent data reviews, mapping and trend visualization, and management meetings focused on recent activity. Adoption timing in this dataset was compiled from public sources such as department websites, press releases, and news archives, and is coded as \u201con\u201d starting in the first documented year of operation.\n\nEach row represents a department-year and includes: the department identifier, the year, the count of arrests for UCR Part II offenses, the share of all arrests that are Part II, whether a CompStat\u2011style system was in use, and the city\u2019s population. Because arrests are administrative measures of police activity and recording practices, they are not the same as offenses known to police or victimization; nonetheless, UCR\u2019s standardized categories enable comparisons across cities and over time. The panel captures a period of substantial organizational change in U.S. municipal policing, during which many large departments modernized data systems and management processes while continuously reporting arrest totals to the UCR program.","file_sha256":"44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"redistributed by CausalReasoningBenchmark (MIT) under the original replication data's terms","member":null,"object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0008/data/data.csv"},"ref_adopt":{"columns":null,"description":"Each agency's first year with HASCOMPSTAT == 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Adjusting for it would condition on a post-treatment mediator and mechanically alter the outcome definition, inducing over-control/mediation bias. The guidance explicitly warns: \u201cthe general message \u2026 is that explicitly adjusting for post-treatment variables is almost always a bad idea\u201d and \u201cwhen we are controlling for a post-treatment variable that mediates the effect of the treatment \u2026 we are only capturing direct effects,\u201d (Applied Causal Inference Powered by ML and AI, Sec. 11.3 Post-Treatment Variables; Mediation; Fig. 11.9a).","strategy":"Difference-in-Differences","time_variable":"YEAR","treatments":["HASCOMPSTAT"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.Treat_Post"]},"naive":{"dataset":"ds_0008","treatment":"HASCOMPSTAT","y":"SHAREPT2"},"native_id":"15","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"AGENCY","controls":["Treat","Post"],"dataset":"ref_panel","x":["Treat_Post"],"y":"SHAREPT2"},"tool":"infer.ols","why":"smf.ols('SHAREPT2 ~ Treat*Post'), cluster by AGENCY"}]},"reference_path":{"calls":[{"args":{"cluster":"AGENCY","controls":["Treat","Post"],"dataset":"ref_panel","x":["Treat_Post"],"y":"SHAREPT2"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_adopt","ref_panel","ref_years"],"source_id":"crb_papers.q015.script"}},"question":"Did cities that adopted CompStat in 1996 experience a change in the share of total arrests that were minor (Part II) offenses from 1995 to 1997 relative to cities that had not yet adopted (1998+)?","requires":[],"runner":null,"sources":["crb_papers.q015.gold","crb_papers.q015.script"],"split":"public","suite":"crb_papers","tags":["econ-bench","external","crb","did","ref_benchmark_script"],"target":"agent","tool":null,"truth":"CausalReasoningBenchmark gold at the pinned revision: causal_queries.json effect and standard_error (the value the item's estimation.py prints), scored as evaluate_result.py scores estimate_within_solution_ci"}