README.md

---
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:

<table>
  <tr>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/parabola.png" width="250"/><br/>parabola
    </td>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/convex.png" width="250"/><br/>convex
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/magnetic.png" width="200"/><br/>magnetic
    </td>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/pendulum.png" width="200"/><br/>pendulum
    </td>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/reflection.png" width="200"/><br/>reflection
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/seesaw.png" width="200"/><br/>seesaw
    </td>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/spring.png" width="200"/><br/>spring
    </td>
    <td align="center">
      <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/water_flow.png" width="200"/><br/>water_flow
    </td>
  </tr>
</table>

<!-- - `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. -->


## 🗂️ 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 |

### 🌐 Multi-View Real-World Scenes

| Config Name | Description |
| ------------------------------- | ------------------------------------------ |
| `MV_Pendulum` | Multi-view real-world pendulum motion |
| `MV_H2_linear` | Multi-view H2 linear scene |
| `MV_H2_nonlinear` | Multi-view H2 nonlinear scene |
| `MV_H3_v_structure_linear` | Multi-view H3 V-structure linear scene |
| `MV_H4_fully_connected_linear` | Multi-view H4 fully connected linear scene |
| `MV_H4_v_structure_linear` | Multi-view H4 V-structure linear scene |
| `MV_H4_v_structure_nonlinear` | Multi-view H4 V-structure nonlinear scene |
| `MV_H5_fully_connected_linear` | Multi-view H5 fully connected linear scene |
| `MV_H5_v_structure_linear` | Multi-view H5 V-structure linear scene |
| `MV_H5_v_structure_nonlinear` | Multi-view H5 V-structure nonlinear scene |

## 📚 Usage

#### 🔹 Load from Hugging Face

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

```python
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.