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README.md

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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)**.
+
+## 🖼️ 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. -->
+
+
+## 📚 Usage
+
+#### 🔹 Option 1: Load from Hugging Face
+
+You can easily load a specific scene using the Hugging Face `datasets` library:
+
+```python
+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**](https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset) + Croissant
+```python
+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())
+```
+
+---
+
+## 📌 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.
+
+---
+
+## 🎯 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.
+
+---
+
+
+
+<!-- ## 🔍 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"] -->