Dataset Viewer
Auto-converted to Parquet Duplicate
id
stringclasses
10 values
graph_nodes
stringclasses
5 values
graph_edges
stringclasses
5 values
intervention
stringclasses
5 values
baseline_state
stringclasses
5 values
counterfactual_state
stringclasses
10 values
predicted_changes
stringclasses
10 values
expected_changed_nodes
stringclasses
5 values
expected_unchanged_nodes
stringclasses
4 values
separation_label
stringclasses
3 values
separation_failure_signature
stringclasses
6 values
constraints
stringclasses
1 value
gold_checklist
stringclasses
9 values
CIG-CSI-001
X,M,Y,Z
X->M;M->Y
do(X=1)
X=0,M=0,Y=0,Z=0
X=1,M=1,Y=1,Z=0
Model: M,Y change; Z stable
M,Y
Z
separated
none
Under 140 words.
1 only descendants change
CIG-CSI-002
X,M,Y,Z
X->M;M->Y
do(X=1)
X=0,M=0,Y=0,Z=0
X=1,M=1,Y=1,Z=1
Model: Z changes too
M,Y
Z
bleed
non-descendant-change
Under 140 words.
1 Z must stay
CIG-CSI-003
A,B,C,D
A->B;A->C
do(A=0)
A=1,B=1,C=1,D=1
A=0,B=0,C=0,D=1
Model: B,C change; D stable
B,C
D
separated
none
Under 140 words.
1 siblings change; D stable
CIG-CSI-004
A,B,C,D
A->B;A->C
do(A=0)
A=1,B=1,C=1,D=1
A=0,B=0,C=0,D=0
Model: D changes with no edge
B,C
D
bleed
spurious-cascade
Under 140 words.
1 D must not change
CIG-CSI-005
S,T,U,V
S->T;T->U;S->V
do(T=0)
S=1,T=1,U=1,V=1
S=1,T=0,U=0,V=1
Model: U changes; V stable
U
V
separated
none
Under 140 words.
1 V not descendant of T
CIG-CSI-006
S,T,U,V
S->T;T->U;S->V
do(T=0)
S=1,T=1,U=1,V=1
S=0,T=0,U=0,V=0
Model: everything changes
U
V
global-drift
everything-changes
Under 140 words.
1 S not intervened
CIG-CSI-007
R,P,Q,W
R->P;R->Q
do(P=1)
R=0,P=0,Q=0,W=0
R=0,P=1,Q=1,W=0
Model: Q changes though not child of P
P
Q,W
bleed
wrong-descendant
Under 140 words.
1 Q depends on R not P
CIG-CSI-008
R,P,Q,W
R->P;R->Q
do(P=1)
R=0,P=0,Q=0,W=0
R=0,P=1,Q=0,W=0
Model: only P changes
P
Q,W
separated
none
Under 140 words.
1 Q stable
CIG-CSI-009
X,Y,Z
X->Y
do(X=0)
X=1,Y=1,Z=1
X=0,Y=0,Z=1
Model: Y changes; Z stable
Y
Z
separated
none
Under 140 words.
1 Z stable
CIG-CSI-010
X,Y,Z
X->Y
do(X=0)
X=1,Y=1,Z=1
X=0,Y=0,Z=0
Model: Z changes without edge
Y
Z
bleed
unlinked-node-change
Under 140 words.
1 Z must stay

What this dataset tests

Whether a counterfactual respects causal separation.

Change one node.

Only descendants should change.

Non-descendants should remain stable.

Why this exists

Models often treat counterfactuals like stories.

They let everything drift.

This benchmark tests graph discipline.

Data format

Each row contains

  • a node set and edge list
  • an intervention do(X=...)
  • baseline and counterfactual states
  • expected changed nodes
  • expected unchanged nodes

Labels

  • separated
  • bleed
  • global-drift

Bleed means a non-descendant changed.

Global-drift means unrelated nodes drifted broadly.

Suggested prompt wrapper

System

You evaluate whether the counterfactual respects causal separation.

User

Nodes
{graph_nodes}

Edges
{graph_edges}

Intervention
{intervention}

Baseline
{baseline_state}

Counterfactual
{counterfactual_state}

Return

  • one label
  • one sentence naming which nodes violated separation

Citation

ClarusC64 dataset family

Downloads last month
17