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README.md

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-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+---
+
+# 🧠 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)**.
+
+---
+
+## 📌 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.
+- `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.
+
+---
+
+## 🎯 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"] -->