Panel: Challenges and opportunities of causality
- Eric Horvitz, Microsoft; Yoshua Bengio, Mila – Quebec AI Institute; Susan Athey, Stanford University; Judea Pearl, UCLA
- Microsoft Research Summit 2021 | Causal Machine Learning
What is causal machine learning? Is it the same as causality research? What are the recent advances and future opportunities? This panel is a unique session where you can hear world-leading researchers’ thoughts on causal machine learning in economics, computer science, statistics, and healthcare. Microsoft Chief Scientific Officer Eric Horvitz will discuss the advances and impact of causal machine learning with four esteemed professors in causal machine learning with different backgrounds. You will hear their thought on why we need causal machine learning now, how causal machine learning can make a real-world impact, and the future of causal machine learning.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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Eric Horvitz
Chief Scientific Officer
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Judea Pearl
Professor
UCLA
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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
