license: apache-2.0
language:
- en
size_categories:
- 10K<n<100K
🧠 Causal3D: A Benchmark for Visual Causal Reasoning
Causal3D is a dataset 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 |
🗂️ Available Scenes
Below is the full list of builder configs you can load using load_dataset.
🔬 Hypothetical Scenes
| Config Name | Description |
|---|---|
Hypothetical_V2_linear |
2 variables, linear causal relationship |
Hypothetical_V2_nonlinear |
2 variables, non-linear causal relationship |
Hypothetical_V3_fully_connected_linear |
3 variables, fully connected, linear |
Hypothetical_V3_v_structure_linear |
3 variables, V-structure, linear |
Hypothetical_V3_v_structure_nonlinear |
3 variables, V-structure, non-linear |
Hypothetical_V4_linear |
4 variables, linear causal relationship |
Hypothetical_V4_v_structure_nonlinear |
4 variables, V-structure, non-linear |
Hypothetical_V4_v_structure_linear |
4 variables, V-structure, linear |
Hypothetical_V5_linear |
5 variables, linear causal relationship |
Hypothetical_V5_v_structure_linear |
5 variables, V-structure, linear |
Hypothetical_V5_v_structure_nonlinear |
5 variables, V-structure, non-linear |
🌍 Real-World Scenes
| Config Name | Description |
|---|---|
Real_Parabola |
Real-world parabola trajectory |
Real_Magnet |
Real-world magnetic force |
Real_Spring |
Real-world spring oscillation |
Real_Water_flow |
Real-world water flow dynamics |
Real_Seesaw |
Real-world seesaw balance physics |
Real_Reflection |
Real-world light reflection |
Real_Pendulum |
Real-world pendulum motion |
Real_Convex_len |
Real-world convex lens refraction |
📚 Usage
🔹 Load from Hugging Face
You can easily load a specific scene using the Hugging Face datasets library:
from datasets import load_dataset
ds = load_dataset(
"LLDDSS/Causal3D_Dataset",
name="Real_Parabola", # Replace with desired scene config name
trust_remote_code=True # Required for custom dataset loading
)
print(ds)
📌 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.
🎯 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.