CausalCascade

CausalCascade is a simulator-grounded benchmark for evaluating causal cascade generation in image-to-video models: generating a complete multi-stage physical process in which initiating events propagate through causally dependent interactions to a terminal outcome. The task tests faithful realization of a causal process, not merely a plausible event or final state.

The release contains 50 event-rich Blender scenes, structured causal event graphs, 872 graph-grounded diagnostic questions, three levels of future-event specification, and 2,832 controlled counterfactual variants. A subset of 225 matched factual-counterfactual pairs supports evaluation of selective responsiveness to physical interventions.

Evaluation

The benchmark supports factual causal-cascade evaluation under three levels of future-event specification and paired counterfactual evaluation under delete, move, scale, and mass interventions. Motion-based metrics and graph-grounded diagnostic VQA provide complementary measures of physical evolution and causal-process fidelity.

See the evaluation protocol for prompt conditions, metrics, and scoring.

Load metadata

from datasets import load_dataset

repo = "deepdeltalab/CausalCascade"
base = load_dataset(repo, "base_scenes", split="test")
counterfactuals = load_dataset(repo, "counterfactuals", split="test")
rq2 = load_dataset(repo, "rq2_pairs", split="test")
qa = load_dataset(repo, "diagnostic_qa", split="test")

The base_scenes and rq2_pairs viewers display native image and video columns; counterfactuals displays counterfactual-variant metadata. Original *_path columns remain repository-relative paths. Use the included causalcascade wrapper to resolve and decode media lazily:

from torch.utils.data import DataLoader
from causalcascade import CausalCascadeDataset, collate_fn

dataset = CausalCascadeDataset(
    repo_id=repo,
    subset="rq2_pairs",
    decode_images=True,
    decode_video=True,
    video_size=(480, 854),
    video_frame_stride=2,
)
loader = DataLoader(dataset, batch_size=1, num_workers=4, collate_fn=collate_fn)
batch = next(iter(loader))

Install the wrapper from a clone of this repository with pip install -e ".[video]". See docs/LOADING.md, docs/SCHEMA.md, and docs/EVALUATION.md.

Only the 225 official RQ2 variants include a rendered counterfactual initial frame and reference rollout in this release. The remaining variants provide reproducible metadata deltas but no counterfactual media. Retained variants include changes to downstream event structure as well as finer-grained motion or timing changes.

License

CausalCascade's original dataset content is licensed under CC BY 4.0. Third-party assets retain their original licenses.

Some .blend source scenes and rendered media incorporate textures, HDRIs, and 3D models from Poly Haven, released under CC0 1.0. See ATTRIBUTION.md.

Citation (TBD)

@misc{causalcascade2026,
  title         = {A Plausible Future Is Not a Causal Future: Can Video Models Generate How the World Unfolds?},
  author        = {AUTHOR_LIST},
  year          = {2026},
  eprint        = {ARXIV_ID},
  archivePrefix = {arXiv},
  primaryClass  = {ARXIV_CATEGORY},
  url           = {https://arxiv.org/abs/ARXIV_ID}
}