Dataset Viewer
Auto-converted to Parquet Duplicate
scm_id
int64
0
10.8k
structure
stringclasses
8 values
tier
int64
1
3
n_vars
int64
2
4
length
int64
200
200
x_obs
listlengths
400
800
x_int
listlengths
400
800
intervention_json
stringlengths
113
123
query_target
listlengths
1
1
query_time
listlengths
1
1
y_true
listlengths
1
1
metadata_json
stringlengths
37
57
0
rct_no_confounding
1
2
200
[ 0.9693785309791565, -0.5417544841766357, 1.0846703052520752, -0.5288801193237305, 0.5321100950241089, -0.7157378792762756, 0.925137996673584, -0.6153379082679749, 0.6877716779708862, -1.1465768814086914, 0.9598890542984009, -0.4032523036003113, 0.6958329677581787, -0.35007500648498535, 0...
[ 0.7244783639907837, -0.39091819524765015, 0.9676430821418762, -0.44017595052719116, 1.055754542350769, -0.4967145323753357, 0.40109920501708984, 0.04703143239021301, 0.6161428689956665, -0.40519148111343384, 1.012902021408081, -1.0377511978149414, 1.2610797882080078, -0.9396472573280334, ...
{"targets": [0], "times": [88], "intervention_type": "hard", "values": {"kind": "scalar", "data": 2.836470365524292}}
[ 1 ]
[ 0.4399999976158142 ]
[ -0.3132737874984741 ]
{"tier": 1, "y_causal_effect": [-0.3132737874984741]}
1
rct_no_confounding
1
2
200
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
{"targets": [0], "times": [174], "intervention_type": "hard", "values": {"kind": "scalar", "data": -1.6565277576446533}}
[ 1 ]
[ 0.8700000047683716 ]
[ 0 ]
{"tier": 1, "y_causal_effect": [0.0]}
2
rct_no_confounding
1
2
200
[ 0.009413391351699829, 0.5551398396492004, -0.3418341279029846, 0.9122728109359741, -0.1086791604757309, 0.7454724311828613, 0.5410979390144348, 0.7153589129447937, 0.03847678005695343, 0.48842430114746094, 0.13468563556671143, -0.11678627133369446, 0.009347587823867798, 0.42787495255470276...
[ 0.36657190322875977, 0.1885058879852295, 0.2233669012784958, 0.4246344268321991, 0.035698212683200836, 0.8576674461364746, 0.16621547937393188, 0.4375578463077545, 0.6429649591445923, 0.4506080746650696, -0.01671871542930603, 0.2450992614030838, 0.2609739601612091, 0.9391281604766846, -0...
{"targets": [0], "times": [41], "intervention_type": "hard", "values": {"kind": "scalar", "data": -1.7262600660324097}}
[ 1 ]
[ 0.20499999821186066 ]
[ 0.8609133362770081 ]
{"tier": 1, "y_causal_effect": [0.8609133362770081]}
3
rct_no_confounding
1
2
200
[ -0.5054837465286255, 0.27265408635139465, 0.5180715918540955, -0.28527161478996277, 0.1854819506406784, -0.2999470829963684, -0.35148197412490845, 0.28592753410339355, 0.7901004552841187, -0.214401975274086, 0.31505078077316284, -0.3491693139076233, 0.5746058821678162, 0.09049063920974731,...
[ 0.5082569718360901, -0.26322874426841736, -0.200998455286026, -0.12753695249557495, 1.0654624700546265, -0.5354140400886536, -0.20734752714633942, -0.33917883038520813, -0.21865597367286682, 0.39186859130859375, 1.1120169162750244, -0.5361505746841431, 0.544404923915863, -0.936631321907043...
{"targets": [0], "times": [166], "intervention_type": "hard", "values": {"kind": "scalar", "data": 0.3944391906261444}}
[ 1 ]
[ 0.8299999833106995 ]
[ -0.7733966708183289 ]
{"tier": 1, "y_causal_effect": [-0.7733966708183289]}
4
rct_no_confounding
1
2
200
[ -0.20297355949878693, 0.645739734172821, -0.2806437015533447, -0.4631645977497101, 0.5284067392349243, 0.5757828950881958, -0.0007417164742946625, 0.23765741288661957, 0.18081869184970856, 0.1974153369665146, 0.45720967650413513, -0.011395150795578957, 0.35246753692626953, 0.10558962076902...
[ 0.5430747270584106, -0.008696844801306725, 0.08307504653930664, -0.5464827418327332, -0.007087453734129667, 0.29719284176826477, 0.0751650407910347, -0.7787648439407349, 0.6383066177368164, 0.33223623037338257, -0.4521658718585968, 0.18838776648044586, 0.5378488898277283, 0.162470400333404...
{"targets": [0], "times": [127], "intervention_type": "hard", "values": {"kind": "scalar", "data": 0.9098840355873108}}
[ 1 ]
[ 0.6349999904632568 ]
[ 0.25535255670547485 ]
{"tier": 1, "y_causal_effect": [0.25535255670547485]}
5
rct_no_confounding
1
2
200
[ 0.09955307096242905, -0.9722410440444946, -0.08427566289901733, -1.1716606616973877, -0.08143587410449982, -0.46879953145980835, 1.1851693391799927, 0.3792358934879303, 0.005069832317531109, -0.3915352523326874, 0.3674892783164978, -0.6310539841651917, 0.22863072156906128, -0.4990564286708...
[ 0.2839958369731903, -0.18192243576049805, -0.060288865119218826, -0.495516836643219, 0.08156170696020126, -0.5743794441223145, 0.13577941060066223, -0.9151149988174438, -0.3285571038722992, -1.2524888515472412, 0.34953075647354126, -0.6102460026741028, 0.12417954951524734, 0.00742512941360...
{"targets": [0], "times": [168], "intervention_type": "hard", "values": {"kind": "scalar", "data": 2.7766692638397217}}
[ 1 ]
[ 0.8399999737739563 ]
[ 0.5893336534500122 ]
{"tier": 1, "y_causal_effect": [0.5893336534500122]}
6
rct_no_confounding
1
2
200
[ -0.03428906202316284, 0.28797659277915955, 0.47105884552001953, -0.08570267260074615, 0.19568724930286407, 0.2627132833003998, -0.13343876600265503, 0.00038943439722061157, 0.6936806440353394, 0.38959917426109314, 0.0494530126452446, 0.26234689354896545, 0.14292852580547333, 0.019495889544...
[ 0.3519328832626343, -0.5611556172370911, 0.31272152066230774, 0.10561199486255646, 0.4590147137641907, 0.13830068707466125, -0.7395783066749573, -0.2974206507205963, 1.1656086444854736, 0.19410841166973114, 0.2323898822069168, 0.05323269963264465, 0.026848215609788895, -0.15629059076309204...
{"targets": [0], "times": [96], "intervention_type": "hard", "values": {"kind": "scalar", "data": -1.9860340356826782}}
[ 1 ]
[ 0.47999998927116394 ]
[ 0.5349301695823669 ]
{"tier": 1, "y_causal_effect": [0.5349301695823669]}
7
rct_no_confounding
1
2
200
[ -1.2651947736740112, 0.14956176280975342, -1.541196584701538, -0.04830790311098099, -1.3453024625778198, 0.08634471893310547, -1.3516709804534912, -0.000919148325920105, -0.7991292476654053, -0.5051231384277344, -0.3688846528530121, 0.08115111291408539, -0.024132907390594482, -0.1679922044...
[ -0.011160016059875488, 0.43074706196784973, -0.19541405141353607, -0.42826908826828003, -0.6126941442489624, -0.21524205803871155, -0.041645824909210205, -0.41103294491767883, -0.2106112241744995, 0.20909494161605835, 0.15599966049194336, -0.39580637216567993, 0.2918436825275421, -0.451017...
{"targets": [0], "times": [164], "intervention_type": "hard", "values": {"kind": "scalar", "data": 2.686105966567993}}
[ 1 ]
[ 0.8199999928474426 ]
[ -0.06523359566926956 ]
{"tier": 1, "y_causal_effect": [-0.06523359566926956]}
8
rct_no_confounding
1
2
200
[ 0.9856379628181458, -0.3881203532218933, -0.46938085556030273, 0.16407302021980286, 0.7086185216903687, -0.17130573093891144, 0.2297116219997406, -0.29205888509750366, 0.2181815505027771, -0.026847243309020996, -0.09255445003509521, -0.44529491662979126, 0.03150290250778198, -0.08292617648...
[ 0.6856710910797119, -0.24839983880519867, 0.35981735587120056, -0.0009001195430755615, -0.04335865378379822, 0.3138904869556427, -0.00748017430305481, -0.313775897026062, 0.5437142848968506, -0.1814325749874115, 0.2906312346458435, 0.19010385870933533, -0.014127910137176514, -0.23632694780...
{"targets": [0], "times": [151], "intervention_type": "hard", "values": {"kind": "scalar", "data": -1.3859379291534424}}
[ 1 ]
[ 0.7549999952316284 ]
[ -0.5630411505699158 ]
{"tier": 1, "y_causal_effect": [-0.5630411505699158]}
9
rct_no_confounding
1
2
200
[ 0.44933590292930603, 0.5355302095413208, 0.6252099275588989, 0.3634485602378845, 0.7374148368835449, 0.5530340075492859, 1.0450154542922974, 0.6805895566940308, 0.6520475745201111, 0.37655818462371826, 0.6129888892173767, 0.42237454652786255, 0.5948319435119629, 0.16820012032985687, 0.50...
[ 0.6916193962097168, 0.1786576211452484, 0.34801533818244934, 0.4922863245010376, 0.38242489099502563, -0.6214491724967957, 0.6445004343986511, -0.08233118057250977, 0.10391616821289062, -0.16868755221366882, 0.653781533241272, 0.23603901267051697, 0.35524773597717285, 0.5733530521392822, ...
{"targets": [0], "times": [127], "intervention_type": "hard", "values": {"kind": "scalar", "data": -4.860935211181641}}
[ 1 ]
[ 0.6349999904632568 ]
[ -0.08084951341152191 ]
{"tier": 1, "y_causal_effect": [-0.08084951341152191]}
10
rct_no_confounding
1
2
200
[ -0.06070845201611519, 0.732067346572876, -0.14664539694786072, 0.351028710603714, 0.1470881849527359, 0.5778157711029053, -0.18140225112438202, 0.3935549557209015, 0.6699851751327515, 1.3619555234909058, 0.37901073694229126, 0.8500353097915649, -0.1739567071199417, 0.3665831387042999, -0...
[ 0.05721740052103996, 0.26297813653945923, 0.49098631739616394, 0.45298129320144653, -0.0333598256111145, 0.721809446811676, 0.26132071018218994, 0.8675960898399353, 0.16915637254714966, 0.923833966255188, 0.6108705401420593, 0.9600124359130859, -0.12354101240634918, 0.16873258352279663, ...
{"targets": [0], "times": [49], "intervention_type": "hard", "values": {"kind": "scalar", "data": 2.1872570514678955}}
[ 1 ]
[ 0.24500000476837158 ]
[ 0.8571581244468689 ]
{"tier": 1, "y_causal_effect": [0.8571581244468689]}
11
rct_no_confounding
1
2
200
[ 0.6661253571510315, -0.03449004888534546, 0.684601902961731, -0.8346775770187378, 0.6042354106903076, 0.08987472951412201, 0.7660762667655945, -0.6813172101974487, 0.7118062973022461, -0.3170134425163269, 0.13743683695793152, -0.3376002907752991, 0.2546135485172272, -0.11981239914894104, ...
[ 0.26983320713043213, -0.06884942948818207, 0.23914343118667603, 0.23500066995620728, 0.5178411602973938, -0.5537115335464478, 0.8801772594451904, -0.5275431871414185, 0.3520582616329193, -0.18446084856987, 0.10463196039199829, -0.20707657933235168, 0.331798255443573, -0.12314517050981522, ...
{"targets": [0], "times": [56], "intervention_type": "hard", "values": {"kind": "scalar", "data": -0.2843243181705475}}
[ 1 ]
[ 0.2800000011920929 ]
[ 0.4148118495941162 ]
{"tier": 1, "y_causal_effect": [0.4148118495941162]}
End of preview. Expand in Data Studio

dot-Identifiability-v1

A frozen evaluation suite from DoTime.

  • Episodes: 10800
  • Schema: parquet shards + manifest.json (md5-checksummed), Croissant metadata.
  • Load with:
from dotime.benchmarks import load_benchmark
suite = load_benchmark("dot-Identifiability-v1")   # pulls this repo at tag v1.0.0

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

Downloads last month
53