Conceitos essenciais
Understanding the identifiability of latent causal models through distribution shifts is crucial for predicting under unseen distributions.
Resumo
This article delves into Identifiable Latent Neural Causal Models, focusing on causal representation learning and the identification of causal variables. The content is structured as follows:
- Introduction to Causal Representation Learning and Identifiability.
- Identifiable Latent Additive Noise Models by Leveraging Distribution Shifts.
- Partial Identifiability Results in scenarios with limited distribution shifts.
- Extension to Identifiable Latent Post-Nonlinear Causal Models.
- Learning Latent Additive Noise Models through Distribution Shifts.
- Experiments conducted on Synthetic Data, Image Data, and fMRI Data.
- Impact Statement and References.
1. Introduction
- Causal representation learning uncovers latent variables dictating system behavior.
- Seen distribution shifts aid in identifying causal representations for predictions under unseen distributions.
2. Identifiable Latent Additive Noise Models by Leveraging Distribution Shifts
- Establishes conditions for identifiability in latent additive noise models using distribution shifts.
3. Partial Identifiability Result
- Addresses scenarios where only a subset of distribution shifts meet identifiability conditions.
4. Extension to Identifiable Latent Post-Nonlinear Causal Models
- Generalizes identifiability results to post-nonlinear models with invertible nonlinear mappings.
5. Learning Latent Additive Noise Models by Leveraging Distribution Shifts
- Translates theoretical findings into a practical method using MLPs for learning latent causal models.
6. Experiments
- Conducted on Synthetic Data, Image Data, and fMRI Data to validate proposed methods.
- Comparative analysis shows superior performance of MLPs in recovering latent causal structures.
7. Impact Statement
- Work aims to advance Machine Learning without specific highlighted societal consequences.
Estatísticas
"Our empirical experiments on synthetic data, image data, and real-world fMRI data serve to demonstrate the effectiveness of our proposed approach."
"The proposed method demonstrates satisfactory results, supporting our identifiability claims."