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Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
Language model activations entangle concepts that mediate their behavior, making it difficult to interpret these factors, which has …
Neural Information Processing Systems (NeurIPS),  2025.
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The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many …
Causal Learning and Reasoning (CLeaR),  2025.
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A Sparsity Principle for Partially Observable Causal Representation Learning
A Sparsity Principle for Partially Observable Causal Representation Learning
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all …
International Conference on Machine Learning (ICML),  2024.
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Multi-View Causal Representation Learning with Partial Observability
Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as …
International Conference of Learning Representations (ICLR),  2024.
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Towards Disentangled High-level Causal Explanations in Text
In this work, we propose a causal representation learning framework for learning disentangled and intervenable high-level explanations …
Mid-Atlantic Student Colloquium on Speech, Language and Learning,  2024.
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A Sparsity Principle for Partially Observable Causal
A Sparsity Principle for Partially Observable Causal
Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods …
Workshop at the Neural Information Processing Systems (NeurIPS),  2023.
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Multi-View Causal Representation Learning with Partial Observability
Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as …
Workshop at the Neural Information Processing Systems (NeurIPS),  2023.
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Using Confounded Data in Latent Model-Based Reinforcement Learning
Using Confounded Data in Latent Model-Based Reinforcement Learning
In the presence of confounding, naively using off-the-shelf offline reinforcement learning (RL) algorithms leads to sub-optimal …
Transactions on Machine Learning Research (TMLR),  2023.
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Invariant Causal Set Covering Machines
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …
Workshop on Spurious Correlations, Invariance, and Stability (ICML),  2023.
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Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widely recognized, with causal discovery and inference being inherently …
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),  2023.
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