🧠 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).

🖼️ Visual Previews

Below are example images from different Causal3D scenes:

parabola	convex
magnetic	pendulum	reflection
seesaw	spring	water_flow

📚 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"
)

record_sets = croissant_dataset.metadata.record_sets
print(record_sets)

df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid))
print(df.head())

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📌 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.

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🧩 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.

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🎯 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

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📊 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.

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