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license: cc-by-nc-nd-4.0
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# 🧠 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)**.
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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.
- `metadata.csv`: Instance-level annotations including object attributes and positions.
<!-- - `causal_graph.json`: Ground-truth causal structure (as adjacency matrix or graph).
- `view_info.json`: Camera/viewpoint metadata.
- `split.json`: Recommended train/val/test splits for benchmarking.
-->
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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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<!-- ## 🔍 Example Use Case
```python
from causal3d import load_scene_data
scene = "SpringPendulum"
data = load_scene_data(scene, split="train")
images = data["images"]
metadata = data["table"]
graph = data["causal_graph"] --> |