--- license: cc-by-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. - `tabular.csv`: Instance-level annotations including object attributes in causal graph. ## 🖼️ Visual Previews Below are example images from different Causal3D scenes:

parabola	convex
magnetic	pendulum	reflection
seesaw	spring	water_flow

--- ## 🎯 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. ---