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Each row corresponds to a state midterm legislative election year, and variables record (a) how the Democratic two\u2011party seat share changed from the last cycle and (b) the Democratic win/lose margin in the immediately preceding gubernatorial election. The coverage used in the original study spans 1882\u20132010, focusing on lower chambers because most upper chambers are staggered and often small. Cases with nonpartisan legislatures or strong third\u2011party dominance were excluded, and the analysis centers on years when the governor was not on the ballot but the lower house was.\n\nThe data were assembled from historical administrative sources: state legislative seat counts from Dubin (2007) with recent updates from the National Conference of State Legislatures, and gubernatorial election returns from ICPSR compilations and state election officials. The extract you have contains just the variables required to reproduce the core state\u2011level midterm seat\u2011change analysis.","file_sha256":"01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0","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":"01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0","transforms":[],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0070/data/data.csv"},"ref_window":{"columns":null,"description":"Rows with dem_lhpct_chng and D_1 both present and |D_1| <= 3 (the script's window)","file_sha256":"01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0","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":"reference","sha256":"01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0","transforms":[{"args":{"columns":["dem_lhpct_chng","D_1"]},"op":"select"},{"args":{"columns":["dem_lhpct_chng","D_1"]},"op":"dropna"},{"args":{"query":"D_1 >= -3 and D_1 <= 3"},"op":"filter"}],"url":"https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/data/ds_0070/data/data.csv"}},"difficulty":"M","id":"crb_papers.q104","level":"T2","meta":{"agent_reach":{"blocked_by":["reference_frame:ref_window","needs_op:rdrobust_nn_variance"],"dtypes":{"ds_0070":{"dem_lhpct_chng":"float64"}},"named":{"outcome":["dem_lhpct_chng"],"treatment":[]},"reachable":false,"rule":"magi_bench.reach/1"},"answer_format":{"figure_transform":"identity","round_to":null,"unit_scale":1.0},"cluster":"ds_0070","crb":{"data_path":"data/ds_0070/data/data.csv","dataset_id":"ds_0070","gold":{"ci95":[-10.960643241211132,2.347401511462577],"ci_rule":"effect \u00b1 1.96\u00b7|standard_error| (evaluate_result.ci_bounds)","effect":-4.306620864874278,"standard_error":3.3949093756820687},"identification":{"causal_quantity":"CATE","cutoff":0,"outcomes":["dem_lhpct_chng"],"running_variable":"D_1","strategy":"Regression Discontinuity","treatments":[],"variant":"sharp (local linear, 3% margin window)"},"identification_strategy":"Regression Discontinuity"},"headline_keys":{"infer.rdd":["effect"]},"item_caveats":[{"kind":"unstated_bandwidth","note":"the gold is computed at a bandwidth the script fixes (h = 3) that neither the question nor the metadata states, so an agent at a principled bandwidth estimates another quantity"}],"naive":{"dataset":"ds_0070","treatment":"D_1","y":"dem_lhpct_chng"},"native_id":"104","reference":{"cite":1,"note":"Reference path transcribed from the item's own estimation script.","steps":[{"args":{"bandwidth":3.0,"cutoff":0.0,"dataset":"ref_window","kernel":"triangular","polynomial":1,"running":"D_1","y":"dem_lhpct_chng"},"tool":"infer.rdd","why":"rdrobust(y_w, x_w, c=0.0, p=1, kernel='triangular', h=h) with h = 3; the script prints the first (Conventional) entry of coef: the local linear jump at h"}]},"reference_path":{"calls":[{"args":{"bandwidth":3.0,"cutoff":0.0,"dataset":"ref_window","kernel":"triangular","polynomial":1,"running":"D_1","y":"dem_lhpct_chng"},"tool":"infer.rdd"}],"needs":["needs_op:rdrobust_nn_variance"],"provenance":"benchmark_script","quotes":{},"reference_datasets":["ref_window"],"source_id":"crb_papers.q104.script"}},"question":"Among gubernatorial elections decided by very small margins, what is the causal effect of a Democratic (vs. Republican) gubernatorial victory on the subsequent midterm change in the Democratic share of lower-house seats?","requires":[],"runner":null,"sources":["crb_papers.q104.gold","crb_papers.q104.script"],"split":"public","suite":"crb_papers","tags":["econ-bench","external","crb","rd","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"}