metadata
license: cc-by-4.0
language:
- en
size_categories:
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pretty_name: Causal3D
tags:
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- Computer_Vision
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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).
📌 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.