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Reflects the period of permanent community-policing presence established by the state. |\n| bope | Binary indicator equal to 1 in months when the Special Police Operations Battalion (BOPE) or the armed forces maintained an occupation/stabilization presence in the complexo, and 0 otherwise. In the administrative records used here, these occupation periods are coded separately from the permanent posts. |\n| date | Integer month index running from 540 through 677 in this extract; an increase of 1 denotes one month. Use these recorded indices to select periods; no calendar-date conversion is specified here. |\n| autos_rate2 | Monthly rate per 100,000 residents of deaths officially registered as \u201cAutos de Resist\u00eancia\u201d (fatalities during police operations) within the complexo. Incidents were geocoded to complexo boundaries and aggregated by month; rates use population estimates as denominators. |\n| complexo2 | Auxiliary numeric field carried over from the source export related to the complexo dimension; no substantive documentation provided in the materials. |\n\nDataset Context:\nThis panel assembles monthly records for favela complexes in Rio de Janeiro during a period when the state created permanent policing posts in selected territories. It integrates two administrative sources: (1) public security records on deaths registered during police operations (locally termed \u201cAutos de Resist\u00eancia\u201d in the period covered) and (2) program logs detailing when permanent posts were active and when occupation/stabilization forces (BOPE or the armed forces) were present. Security incidents were geocoded to mapped complex boundaries, aggregated to the complex\u2013month level, and converted to rates per 100,000 using census-based population estimates for each complex.\n\nThe spatial unit\u2014a complexo\u2014groups adjacent favelas that share borders and, in practice, correspond to the operational footprint of policing deployments. The temporal resolution is monthly, spanning indices 540\u2013677, a phase that includes the roll\u2011out and consolidation of permanent posts and earlier occupation phases. In the coding scheme used here, occupation/stabilization months and permanent\u2011post months are tracked with nonoverlapping indicators.\n\nFor the comparison centered on first UPP activation at index 589, the earlier window is 577\u2013588 and the later window is 589\u2013600; comparison complexes remain without an active UPP through 600. For first activation at 606, the windows are 594\u2013605 and 606\u2013617, with comparison complexes remaining without an active UPP through 617. Each window contains 12 months. The outcome for each complex and window is its mean monthly fatal-shooting rate, not a cumulative count; the occupation indicator is likewise averaged within each window.\n\nMeasurement follows local administrative definitions in effect at the time. \u201cAutos de Resist\u00eancia\u201d reflects fatalities recorded as resulting from confrontations with police; these events were manually geocoded and then aggregated to complex\u2013month cells. Rates normalize counts by resident population to facilitate comparisons across differently sized complexes and over time. As with any administrative system, users should be mindful of potential changes in reporting practices, geocoding precision, and population denominators when interpreting temporal or cross\u2011sectional variation.\n","file_sha256":"713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad","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":"713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0023/data/data.csv"},"ref_panel":{"columns":null,"description":"Two rows per unit (window means before and from date 589): units first treated at 589 and units not treated by 600, each with rows in both 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Therefore, we coded BOPEi,t as a binary variable that takes the value of 1 during the period in which BOPE was present in the favela, and Post_UPPi,t as a binary variable that takes the value of 1 after a UPP was introduced.\u201d (Estimating the Effects of the UPP, p. 567). Table 2 further notes: \u201cAll models include a binary variable for the BOPE invasion period, unit and time fixed-effects, and complexo-specific linear time trends.\u201d","strategy":"Difference-in-Differences","time_variable":"date","treatments":["upp_post"]},"identification_strategy":"Difference-in-Differences"},"headline_keys":{"infer.ols":["coef.interaction"]},"naive":{"dataset":"ds_0023","treatment":"upp_post","y":"autos_rate2"},"native_id":"44","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"cluster":"complexo_id","controls":["treated","post","bope"],"dataset":"ref_panel","x":["interaction"],"y":"y"},"tool":"infer.ols","why":"sm.OLS(y, [const, treated, post, interaction, bope]).fit(cov_type='cluster', groups=unit, use_t=True) (CR1 errors with statsmodels' default G/(G-1)\u00b7(N-1)/(N-K) correction)"}]},"reference_path":{"calls":[{"args":{"cluster":"complexo_id","controls":["treated","post","bope"],"dataset":"ref_panel","x":["interaction"],"y":"y"},"tool":"infer.ols"}],"needs":[],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_panel","ref_post","ref_pre","ref_td"],"source_id":"crb_papers.q044.script"}},"question":"Among favela complexes where a UPP first became active at month index 589, how did the average monthly rate of fatal police shootings (Autos de Resistencia) per 100,000 residents change from month indices 577\u2013588 to 589\u2013600, relative to complexes that remained without an active UPP through 600, accounting for BOPE or armed-forces occupation?","requires":[],"runner":null,"sources":["crb_papers.q044.gold","crb_papers.q044.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"}