Research talk: Causal learning: Discovering causal relations for out-of-distribution generalization
- Wei Chen | Microsoft Research
- Microsoft Research Summit 2021 | Causal Machine Learning
Machine learning models should be explainable and robust on out-of-distribution samples, especially on safety-critical tasks such as healthcare, and security. However, current models heavily rely on i.i.d assumption, and are therefore sensitive to OOD data. In this talk, Wei Chen, from the Computing and Learning Theory group at Microsoft Research Asia, will show how causal inference tools can be leveraged to empower machine learning models and make them more robust. To achieve this goal, we propose the causal invariance model, which can eliminate spurious correlations and keep only causal relation for prediction, and we will show both theoretical and empirical proof.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
Causal Machine Learning
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Opening remarks: Causal Machine Learning
- Cheng Zhang
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Research talk: Challenges and opportunities in causal machine learning
- Amit Sharma,
- Cheng Zhang,
- Emre Kiciman
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Research talk: Causal ML and business
- Jacob LaRiviere
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Research talk: Causality for medical image analysis
- Daniel Coelho de Castro
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Research talk: Causal ML and fairness
- Allison Koenecke
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Panel: Causal ML Research at Microsoft
- Adith Swaminathan,
- Javier González Hernández,
- Justin Ding
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Research talk: Post-contextual-bandit inference
- Nathan Kallus
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Demo: Enabling end-to-end causal inference at scale
- Eleanor Dillon,
- Amit Sharma
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Panel: Causal ML at Microsoft
- Juan Lavista Ferres,
- Mingqi Wu,
- Sonia Jaffe
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Panel: Causal ML in industry
- Greg Lewis,
- Ya Xu,
- Totte Harinen
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Closing remarks: Causal Machine Learning
- Emre Kiciman
