--- license: apache-2.0 language: - en size_categories: - 10K

parabola

convex

magnetic

pendulum

reflection

seesaw

spring

water_flow ## 🗂️ 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.