Advancing Causal Representation Learning: Enhancing Robustness and Transferability in Real-World Applications

Abstract

Conventional supervised learning methods heavily depend on statistical inference, often assuming that data is identically and independently distributed (i.i.d). However, this assumption rarely holds in real-world scenarios, where environments or domains frequently shift, posing significant challenges to model robustness and generalization. Moreover, statistical models are typically treated as black boxes, with their learned representations remaining opaque and challenging to interpret. My research addresses these issues through a causal learning perspective, aiming to enhance the interpretability and adaptability of machine learning models in dynamic and uncertain environments.

I have developed innovative methods for learning causal models that are applicable to a wide range of machine learning tasks, including transfer learning, out-of-distribution generalization, reinforcement learning, and action classification. The first method introduces a generative model tailored to learn causal variables in scenarios where the causal graph is known, such as Human Trajectory Prediction. By incorporating domain knowledge, this approach models the underlying causal mechanisms, leading to improved performance on both synthetic and real-world datasets. The results demonstrate that this generative model outperforms traditional statistical models, particularly in out-of-distribution contexts.

The second method targets the more challenging scenario where the causal structure is unknown. I have explored various conditions and assumptions that facilitate the discovery of causal relationships without prior knowledge of the causal graph. This method combines advanced techniques in causal inference and machine learning to uncover the underlying causal graph and variables from observed data. Evaluations on both real-world and synthetic datasets show that this method not only surpasses existing approaches in causal representation learning but also brings AI systems closer to practical, real-world applications by enhancing reliability and interpretability.

Overall, my research contributes significant advancements to the field of causal learning, providing novel solutions that improve model interpretability and robustness. These methods lay a strong foundation for developing AI systems capable of adapting to diverse and evolving real-world conditions, thereby broadening the scope and impact of machine learning across various domains.