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Causality

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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Causal Discovery with Language Models as Imperfect Experts
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, …
Workshop on Structured Probabilistic Inference & Generative Modeling (ICML),  2023.
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Using Confounded Data in Offline RL
Using Confounded Data in Offline RL
In this work we consider the problem of confounding in offline RL, also referred to as the delusion problem. While it is known that …
Workshop at the Neural Information Processing Systems (NeurIPS),  2022.
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Deconfounding Dynamic Treatment Regimes
Deconfounding Dynamic Treatment Regimes
Counterfactual prediction under sequences of actions is a fundamental problem in decision-making. Existing methods in causal inference …
SSC,  2022.
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Typing assumptions improve identification in causal discovery - theory and algorithms
Typing assumptions improve identification in causal discovery - theory and algorithms
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Causal Learning and Reasoning (CLeaR),  2022.
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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the …
Conference on Neural Information Processing Systems (NeurIPS),  2021.
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Typing assumptions improve identification in causal discovery - Report and comments on future directions
Typing assumptions improve identification in causal discovery - Report and comments on future directions
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Workshop at the Neural Information Processing Systems (NeurIPS),  2021.
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Typing assumptions improve identification in causal discovery
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an …
Workshop at the International Conference on Machine Learning (ICML),  2021.
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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing …
Workshop at the International Conference on Machine Learning (ICML),  2021.
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Differentiable Causal Discovery from Interventional Data
Differentiable Causal Discovery from Interventional Data
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the …
Conference on Neural Information Processing Systems (NeurIPS),  2020.
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