Causal Discovery

Causal Discovery #

Causal discovery focuses on learning the causal structure behind observational data, identifying causal relationships between variables.

Recent Literature #

1. A Scalable and General Framework for Privacy-Preserving Causality-Aware X AISTATS, 2024. paper

   Xupeng Cao, Yuming Huang, Zining Zhu, Jing Ma

2. Scalable Computational Methods for Bayesian Additive Regression Trees Journal of Computational and Graphical Statistics, 2021. paper

   Brent R. Linley and Jingyu He and Jesse Windle

3. Causal Inference Using Invariant Prediction: Identification and Little’s Law of Causal Discovery JMLR, 2023. paper

   Andrea Rotnitzky, James M. Robins, Rajeeva Karandikar

4. Learning Temporal Causal Graphs for Approximately Stationary Environments ICML, 2023. paper, code

   Kevin Marx, Jiji Zhang and Kun Zhang

5. Graph neural networks for improved electroencephalographic seizure detection Nature Communications, 2023. paper

   Akshay Gujral and Eleonora Spinelli and Ibrahim Alachiotis and Cosmin Anitescu and Pieter Collins

6. Causal structure learning through deep generative models: Applications to real-world time series in clinical neuroscience ICML, 2024. paper

   Kion Fallah, Tim Suereth, Houman Dreyfuss, et al.

7. Graph Structure Learning for Temporal Reinforcement Learning NeurIPS, 2022. paper

   Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, Rémi Munos, Georg Ostrovski

8. Causal Graph Learning for Large-scale Heterogeneous Biological Networks Nature Machine Intelligence, 2023. paper

   Alexander Statnikov, Constantine F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Melissa Levy

9. Constraint-based Causal Discovery with Mixed Data Machine Learning, 2023. paper

   Jiji Zhang