Big Names: Judy Pearl [Tutorial] [slides] [textbook], James Robin [Textbook] [slides]
Tutorial:
- Causality for machine learning [4]
- Towards Causal Representation Learning [8]
- A briefing on causal inference written by myself
Workshop: NIPS2018 workshop on causal learning, KDD2020 Tutorial on Causal Inference Meets Machine Learning
Material: MILA Course
Causality and disentanglement: [5] [6]
Counterfactual and disentanglement: [7]
Reference
[1] Chalupka K, Perona P, Eberhardt F. Visual causal feature learning. arXiv preprint arXiv:1412.2309, 2014.
[2] Lopez-Paz D, Nishihara R, Chintala S, et al. Discovering causal signals in images. CVPR, 2017.
[3] Bau D, Zhu J Y, Strobelt H, et al. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. arXiv preprint arXiv:1811.10597, 2018.
[4] Bernhard Schölkopf: CAUSALITY FOR MACHINE LEARNING. arXiv preprint arXiv:1911.10500, 2019.
[5] Kim, Hyemi, et al. “Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder.” arXiv preprint arXiv:2011.11878 (2020).
[6] Shen, Xinwei, et al. “Disentangled Generative Causal Representation Learning.” arXiv preprint arXiv:2010.02637 (2020).
[7] Yue, Zhongqi, et al. “Counterfactual Zero-Shot and Open-Set Visual Recognition.” arXiv preprint arXiv:2103.00887 (2021).
[8] Schölkopf, Bernhard, et al. “Towards causal representation learning.” arXiv preprint arXiv:2102.11107 (2021).