The world we live in is causal, yet many Artificial Intellgience systems and most Machine Learning systems ignore this reality for the sake of convenience. There is a growing interest in making progress in this important concept, and this space will highlight our research in that area.
Completed Theses Related to Causality
Advancing Causal Representation Learning: Enhancing Robustness and Transferability in Real-World Applications
UWSpace Thesis Repository,
University of Waterloo,
Feb,
2025.
Conventional supervised learning methods heavily depend on statistical inference, of- ten 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, sta- tistical models are typically treated as black boxes, with their learned representations re- maining 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 gen- eralization, 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 dis- covery 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 learn- ing, 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.
Our Papers on Causality
Modeling Soft Intervention Effects for Implicit Causal Representation Learning
Machine Learning.
115,
(199).
Springer Nature,
Aug,
2026.
Learning causal representations from data without access to ground-truth causal graphs remains a central challenge in representation learning. Implicit approaches—where the model learns causal dependencies without explicitly parameterizing the causal graph—offer a compelling advantage over explicit methods by avoiding optimization difficulties such as local minima associated with adjacency matrix estimation. However, existing implicit methods often assume access to hard interventions, which are rarely feasible in real-world scenarios. In contrast, soft interventions—more prevalent in practice—modify causal mechanisms without severing parental dependencies, introducing subtle, ambiguous effects that confound learning. To address this, we propose ICRL-SM, a novel method for Implicit Causal Representation Learning from soft interventions using a causal mechanism switch variable. This variable captures unwanted changes induced by soft interventions, enabling the model to focus on the necessary variations that reflect underlying causal structure. Our framework leverages a variational autoencoder trained on paired pre- and post-intervention samples, and is theoretically grounded under a set of assumptions that ensure identifiability of causal representations. Although some assumptions (e.g., Gaussianity of latent variables, diffeomorphic decoders) are strict, we show empirically that our method performs robustly even when these are violated—demonstrating strong results on both synthetic benchmarks and real-world image datasets. These findings highlight the potential of ICRL-SM to bridge the gap between theoretical identifiability and practical applicability, advancing causal representation learning under realistic conditions. The source code for this paper is available at: https://github.com/sshirahmad/ICRL.
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
In
Proceedings of the 40th International Conference on Machine Learning (ICML).
PMLR,
Honolulu, Hawaii, USA.
Jul,
2023.
Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge transfer under distribution shifts. While we evaluate the effectiveness of our proposed method in human trajectory prediction models, GCRL can be applied to other domains as well. First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment. Selection variables are used to determine which parts of the model can be directly transferred to a new environment without fine-tuning. Second, we propose an end-to-end variational learning paradigm to learn the causal mechanisms that generate observations from features. GCRL is supported by strong theoretical results that imply identifiability of the causal model under certain assumptions. Experimental results on synthetic and real-world motion forecasting datasets show the robustness and effectiveness of our proposed method for knowledge transfer under zero-shot and low-shot settings by substantially outperforming the prior motion forecasting models on out-of-distribution prediction.
Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering
In
International Joint Conference on Artificial Intelligence (IJCAI).
Beijing, China.
2013.
We analyze the foundations of cyclic causal models for discrete variables, and compare structural equation models (SEMs) to an alternative semantics as the equilibrium (stationary) distribution of a Markov chain. We show under general conditions, discrete cyclic SEMs cannot have independent noise, even in the simplest case, cyclic structural equation models imply constraints on the noise. We give a formalization of an alternative Markov chain equilibrium semantics which requires not only the causal graph, but also a sample order. We show how the resulting equilibrium is a function of the sample ordering, both theoretically and empirically.