Panel: Causal ML at Microsoft
- Juan Lavista Ferres, Mingqi Wu, Sonia Jaffe, Mehrnoosh Sameki | Microsoft Research, Microsoft, Microsoft Research, Microsoft Azure
- Microsoft Research Summit 2021 | Causal Machine Learning
Causal reasoning and machine learning is widely deployed across Microsoft, to support high-stakes internal decision-making and to build products that help our customers make better use of their own data. This panel, moderated by AI for Good Chief Scientist Juan Lavista Ferres, will quiz some of our leading practitioners on how they use causal tools in their work, what tools are needed but missing, and how they hope causal machine learning will evolve in the future.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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Juan M. Lavista Ferres
Corporate Vice President & Chief Data Scientist
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Mingqi Wu
Principal Data Scientist
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Sonia Jaffe
Principal Researcher
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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
