Linking pages
- New method identifies the root causes of statistical outliers - Amazon Science https://www.amazon.science/blog/new-method-identifies-the-root-causes-of-statistical-outliers 53 comments
- ICML: Where causality meets machine learning - Amazon Science https://www.amazon.science/blog/icml-where-causality-meets-machine-learning 8 comments
- NeurIPS: Why causal-representation learning may be the future of AI - Amazon Science https://www.amazon.science/blog/neurips-why-causal-representation-learning-may-be-the-future-of-ai 0 comments
Linked pages
- Graph (discrete mathematics) - Wikipedia https://en.wikipedia.org/wiki/Graph_(discrete_mathematics) 15 comments
- GitHub - py-why/dowhy: DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. https://github.com/py-why/dowhy 2 comments
- Determining causality in correlated time series - Amazon Science https://www.amazon.science/blog/determining-causality-in-correlated-time-series 0 comments
- Judea Pearl - Wikipedia https://en.wikipedia.org/wiki/Judea_Pearl 0 comments
- Improving explainable AI’s explanations - Amazon Science https://www.amazon.science/blog/improving-explainable-ais-explanations 0 comments
- Explaining changes in real-world data - Amazon Science https://www.amazon.science/blog/explaining-changes-in-real-world-data 0 comments
- DoWhy evolves to independent PyWhy model to help causal inference grow - Microsoft Research https://www.microsoft.com/en-us/research/blog/dowhy-evolves-to-independent-pywhy-model-to-help-causal-inference-grow/ 0 comments
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