README.md

metadata

license: cc-by-4.0
language:
  - en
size_categories:
  - 100K<n<1M
pretty_name: Causal3D
tags:
  - Causality
  - Computer_Vision
dataset_info:
  - config_name: hypothetical_scenes_Hypothetic_v2_linear
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2197142
        num_examples: 14368
    download_size: 0
    dataset_size: 2197142
  - config_name: hypothetical_scenes_Hypothetic_v2_nonlinear
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1809956
        num_examples: 10000
    download_size: 0
    dataset_size: 1809956
  - config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1397093
        num_examples: 10000
    download_size: 0
    dataset_size: 1397093
  - config_name: hypothetical_scenes_Hypothetic_v4_linear_full_connected
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1699598
        num_examples: 10050
    download_size: 0
    dataset_size: 1699598
  - config_name: hypothetical_scenes_Hypothetic_v4_linear_v
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2053379
        num_examples: 10000
    download_size: 0
    dataset_size: 2053379
  - config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2828217
        num_examples: 10000
    download_size: 0
    dataset_size: 2828217
  - config_name: hypothetical_scenes_Hypothetic_v5_linear
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1956461
        num_examples: 10000
    download_size: 0
    dataset_size: 1956461
  - config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1955921
        num_examples: 10000
    download_size: 0
    dataset_size: 1955921
  - config_name: hypothetical_scenes_rendered_h3_linear_128P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 5425498
        num_examples: 15000
    download_size: 0
    dataset_size: 5425498
  - config_name: hypothetical_scenes_rendered_h3_nonlinear_128P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 3239120
        num_examples: 10223
    download_size: 0
    dataset_size: 3239120
  - config_name: hypothetical_scenes_rendered_h5_nonlinear
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 5459126
        num_examples: 10360
    download_size: 0
    dataset_size: 5459126
  - config_name: real_scenes_Real_Parabola
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 1323548
        num_examples: 10000
    download_size: 0
    dataset_size: 1323548
  - config_name: real_scenes_Real_magnet_v3
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 28397
        num_examples: 481
    download_size: 0
    dataset_size: 28397
  - config_name: real_scenes_Real_magnet_v3_5
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 93977
        num_examples: 1503
    download_size: 0
    dataset_size: 93977
  - config_name: real_scenes_Real_parabola_multi_view
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 0
        num_examples: 0
    download_size: 0
    dataset_size: 0
  - config_name: real_scenes_Real_spring_v3_256P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 136325
        num_examples: 450
    download_size: 0
    dataset_size: 136325
  - config_name: real_scenes_Water_flow_scene_render
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2792618
        num_examples: 10000
    download_size: 0
    dataset_size: 2792618
  - config_name: real_scenes_convex_len_render_images
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 72448
        num_examples: 1078
    download_size: 0
    dataset_size: 72448
  - config_name: real_scenes_real_pendulum
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2925963
        num_examples: 9999
    download_size: 0
    dataset_size: 2925963
  - config_name: real_scenes_rendered_magnetic_128
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2324526
        num_examples: 8350
    download_size: 0
    dataset_size: 2324526
  - config_name: real_scenes_rendered_reflection_128P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2765222
        num_examples: 9995
    download_size: 0
    dataset_size: 2765222
  - config_name: real_scenes_seesaw_scene_128P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2275814
        num_examples: 10000
    download_size: 0
    dataset_size: 2275814
  - config_name: real_scenes_spring_scene_128P
    features:
      - name: image
        dtype: image
      - name: file_name
        dtype: string
      - name: metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 2547386
        num_examples: 10000
    download_size: 0
    dataset_size: 2547386

🧠 Causal3D: A Benchmark for Visual Causal Reasoning

Causal3D is a comprehensive benchmark designed to evaluate models’ abilities to uncover latent causal relations from structured and visual data. This dataset integrates 3D-rendered scenes with tabular causal annotations, providing a unified testbed for advancing causal discovery, causal representation learning, and causal reasoning with vision-language models (VLMs) and large language models (LLMs).

📚 Usage

🔹 Option 1: Load from Hugging Face

You can easily load a specific scene using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset(
    "LLDDSS/Causal3D",
    name="real_scenes_Real_Parabola",
    download_mode="force_redownload",  # Optional: force re-download
    trust_remote_code=True             # Required for custom dataset loading
)

print(dataset)

🔹 Option 2: Download via Kaggle + Croissant

import mlcroissant as mlc
import pandas as pd

# Load the dataset metadata from Kaggle
croissant_dataset = mlc.Dataset(
    "https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset/croissant/download"
)

# List available record sets
record_sets = croissant_dataset.metadata.record_sets
print(record_sets)

# Load records from the first record set
df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid))
print(df.head())

---

📌 Overview

While recent progress in AI and computer vision has been remarkable, there remains a major gap in evaluating causal reasoning over complex visual inputs. Causal3D bridges this gap by providing:

• 19 curated 3D-scene datasets simulating diverse real-world causal phenomena.
• Paired tabular causal graphs and image observations across multiple views and backgrounds.
• Benchmarks for evaluating models in both structured (tabular) and unstructured (image) modalities.

---

🧩 Dataset Structure

Each sub-dataset (scene) contains:

• images/: Rendered images under different camera views and backgrounds.
• tabular.csv: Instance-level annotations including object attributes in causal graph.

🖼️ Visual Previews

Below are example images from different Causal3D scenes:

parabola	convex
magnetic	pendulum	reflection
seesaw	spring	water_flow

---

🎯 Evaluation Tasks

Causal3D supports a range of causal reasoning tasks, including:

• Causal discovery from image sequences or tables
• Intervention prediction under modified object states or backgrounds
• Counterfactual reasoning across views
• VLM-based causal inference given multimodal prompts

---

📊 Benchmark Results

We evaluate a diverse set of methods:

• Classical causal discovery: PC, GES, NOTEARS
• Causal representation learning: CausalVAE, ICM-based encoders
• Vision-Language and Large Language Models: GPT-4V, Claude-3.5, Gemini-1.5

Key Findings:

• As causal structures grow more complex, model performance drops significantly without strong prior assumptions.
• A noticeable performance gap exists between models trained on structured data and those applied directly to visual inputs.

---