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scm_id
int64
structure
string
tier
int64
n_vars
int64
length
int64
x_obs
list
x_int
list
intervention_json
string
query_target
list
query_time
list
y_true
list
metadata_json
string
0
back_door
2
3
200
[ 0, 0, 0, -0.7941707372665405, 0.43034985661506653, -0.15542618930339813, 0.20425614714622498, -0.8699097633361816, -0.5548118352890015, -0.0018471344374120235, 0.7590669989585876, -1.3000249862670898, -1.450156569480896, -0.6019259691238403, 1.2371865510940552, -0.9673604369163513, 0...
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{"targets": [0], "times": [120], "intervention_type": "hard", "values": {"kind": "scalar", "data": 0.5162982940673828}}
[ 0 ]
[ 0.6984924674034119 ]
[ 0.5162982940673828 ]
{"tier": 2, "y_causal_effect": [0.9103466272354126]}
1
back_door
2
3
200
[ 0, 0, 0, 0.6052538156509399, 0.8735422492027283, -0.30583566427230835, 0.4109662175178528, -0.6866844296455383, 1.1842283010482788, -0.5989279747009277, 0.6593481302261353, 0.1886502057313919, 0.6226577162742615, -0.7510792016983032, -0.19694721698760986, 0.4344218373298645, -0.75786...
[ 0, 0, 0, 0.6052538156509399, 0.8735422492027283, -0.30583566427230835, 0.4109662175178528, -0.6866844296455383, 1.1842283010482788, -0.5989279747009277, 0.6593481302261353, 0.1886502057313919, 0.6226577162742615, -0.7510792016983032, -0.19694721698760986, 0.4344218373298645, -0.75786...
{"targets": [0], "times": [132], "intervention_type": "hard", "values": {"kind": "scalar", "data": -0.45516228675842285}}
[ 1 ]
[ 0.7939698696136475 ]
[ 1.6366291046142578 ]
{"tier": 2, "y_causal_effect": [0.0]}
2
back_door
2
3
200
[ 0, 0, 0, 0.15836699306964874, -0.02206048183143139, 0.21648293733596802, -0.30645668506622314, 0.43588343262672424, 0.03674467280507088, 0.04111644625663757, -0.05477164685726166, -0.5233263373374939, 0.3849344551563263, 0.08583082258701324, -0.7250988483428955, -0.09117326140403748, ...
[ 0, 0, 0, 0.15836699306964874, -0.02206048183143139, 0.21648293733596802, -0.30645668506622314, 0.43588343262672424, 0.03674467280507088, 0.04111644625663757, -0.05477164685726166, -0.5233263373374939, 0.3849344551563263, 0.08583082258701324, -0.7250988483428955, -0.09117326140403748, ...
{"targets": [0], "times": [108], "intervention_type": "hard", "values": {"kind": "scalar", "data": 0.7704663276672363}}
[ 2 ]
[ 0.6783919334411621 ]
[ 2.0096945762634277 ]
{"tier": 2, "y_causal_effect": [2.9424662590026855]}
3
back_door
2
3
200
[ 0, 0, 0, 1.2486952543258667, 0.43183302879333496, 0.034935880452394485, 0.6961820721626282, -0.08304841071367264, 0.2225024700164795, 0.20775407552719116, 0.263413667678833, 0.5406158566474915, 0.1372484266757965, 0.016639351844787598, -0.2651246190071106, -0.17668402194976807, -0.39...
[ 0, 0, 0, 1.2486952543258667, 0.43183302879333496, 0.034935880452394485, 0.6961820721626282, -0.08304841071367264, 0.2225024700164795, 0.20775407552719116, 0.263413667678833, 0.5406158566474915, 0.1372484266757965, 0.016639351844787598, -0.2651246190071106, -0.17668402194976807, -0.39...
{"targets": [0], "times": [100], "intervention_type": "hard", "values": {"kind": "scalar", "data": -2.2348737716674805}}
[ 0 ]
[ 0.7587939500808716 ]
[ -2.2348737716674805 ]
{"tier": 2, "y_causal_effect": [-1.683584213256836]}
4
back_door
2
3
200
[ 0, 0, 0, 0.1806170642375946, 0.08129721879959106, 0.0024829967878758907, -0.39732643961906433, -0.09197718650102615, 0.030213141813874245, 0.023681938648223877, 0.02441282942891121, 0.15145793557167053, -0.1408080756664276, 0.4917982816696167, 0.21551312506198883, 0.08527018129825592, ...
[ 0, 0, 0, 0.1806170642375946, 0.08129721879959106, 0.0024829967878758907, -0.39732643961906433, -0.09197718650102615, 0.030213141813874245, 0.023681938648223877, 0.02441282942891121, 0.15145793557167053, -0.1408080756664276, 0.4917982816696167, 0.21551312506198883, 0.08527018129825592, ...
{"targets": [0], "times": [98], "intervention_type": "hard", "values": {"kind": "scalar", "data": 0.10256295651197433}}
[ 0 ]
[ 0.49246230721473694 ]
[ 0.10256295651197433 ]
{"tier": 2, "y_causal_effect": [-0.30386853218078613]}
5
back_door
2
3
200
[ 0, 0, 0, 0.052974868565797806, 0.24898885190486908, 0.5382609367370605, 0.1443587988615036, 0.6253760457038879, 0.1380392163991928, -0.460416704416275, 0.33268019556999207, -0.31592103838920593, 0.08309701085090637, -0.7270052433013916, -0.8729592561721802, -0.03268304839730263, -0.0...
[ 0, 0, 0, 0.052974868565797806, 0.24898885190486908, 0.5382609367370605, 0.1443587988615036, 0.6253760457038879, 0.1380392163991928, -0.460416704416275, 0.33268019556999207, -0.31592103838920593, 0.08309701085090637, -0.7270052433013916, -0.8729592561721802, -0.03268304839730263, -0.0...
{"targets": [0], "times": [135], "intervention_type": "hard", "values": {"kind": "scalar", "data": -1.5510822534561157}}
[ 1 ]
[ 0.7587939500808716 ]
[ 0.373957097530365 ]
{"tier": 2, "y_causal_effect": [0.0]}
6
back_door
2
3
200
[ 0, 0, 0, -0.03743857145309448, -0.07008998095989227, -0.3585989773273468, -0.2422066479921341, 0.6470147371292114, -0.27267855405807495, 0.6319953203201294, -0.06403300166130066, -0.09400349855422974, -0.2007257044315338, -0.49686700105667114, -0.2610454559326172, -0.5770569443702698, ...
[ 0, 0, 0, -0.03743857145309448, -0.07008998095989227, -0.3585989773273468, -0.2422066479921341, 0.6470147371292114, -0.27267855405807495, 0.6319953203201294, -0.06403300166130066, -0.09400349855422974, -0.2007257044315338, -0.49686700105667114, -0.2610454559326172, -0.5770569443702698, ...
{"targets": [0], "times": [78], "intervention_type": "hard", "values": {"kind": "scalar", "data": 2.833268165588379}}
[ 1 ]
[ 0.7788944840431213 ]
[ 0.7837820649147034 ]
{"tier": 2, "y_causal_effect": [0.0]}
7
back_door
2
3
200
[ 0, 0, 0, -0.4921431839466095, -0.43736395239830017, -0.6176850199699402, 0.6144384145736694, 0.4573894739151001, 0.5242908000946045, -0.6485086679458618, -0.6499686241149902, -0.5553620457649231, 0.7077164053916931, -0.034741878509521484, 0.6726173758506775, 0.41436707973480225, 0.03...
[ 0, 0, 0, -0.4921431839466095, -0.43736395239830017, -0.6176850199699402, 0.6144384145736694, 0.4573894739151001, 0.5242908000946045, -0.6485086679458618, -0.6499686241149902, -0.5553620457649231, 0.7077164053916931, -0.034741878509521484, 0.6726173758506775, 0.41436707973480225, 0.03...
{"targets": [0], "times": [83], "intervention_type": "hard", "values": {"kind": "scalar", "data": -0.6796891093254089}}
[ 1 ]
[ 0.6331658363342285 ]
[ -1.270862102508545 ]
{"tier": 2, "y_causal_effect": [0.0]}
8
back_door
2
3
200
[0.0,0.0,0.0,0.1093088760972023,-0.44739633798599243,-0.4046906530857086,0.49956685304641724,0.62637(...TRUNCATED)
[0.0,0.0,0.0,0.1093088760972023,-0.44739633798599243,-0.4046906530857086,0.49956685304641724,0.62637(...TRUNCATED)
"{\"targets\": [0], \"times\": [64], \"intervention_type\": \"hard\", \"values\": {\"kind\": \"scala(...TRUNCATED)
[ 1 ]
[ 0.5175879597663879 ]
[ 0.15175166726112366 ]
{"tier": 2, "y_causal_effect": [0.0]}
9
back_door
1
3
200
[0.0,0.0,0.0,-1.35784912109375,0.018763063475489616,0.5442620515823364,-1.608590841293335,-0.1866680(...TRUNCATED)
[0.0,0.0,0.0,-1.35784912109375,0.018763063475489616,0.5442620515823364,-1.608590841293335,-0.1866680(...TRUNCATED)
"{\"targets\": [0], \"times\": [79], \"intervention_type\": \"hard\", \"values\": {\"kind\": \"scala(...TRUNCATED)
[ 1 ]
[ 0.7286432385444641 ]
[ 0.11156467348337173 ]
{"tier": 1, "y_causal_effect": [0.0]}
End of preview. Expand in Data Studio

dot-Continuous-v1

A frozen evaluation suite from DoTime.

  • Episodes: 9999
  • Schema: parquet shards + manifest.json (md5-checksummed), Croissant metadata.
  • Load with:
from dotime.benchmarks import load_benchmark
suite = load_benchmark("dot-Continuous-v1")   # pulls this repo at tag v1.0.0

Generated reproducibly by scripts/build_release.py. Zenodo DOI is the citable archive of record.

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