Opening remarks: Causal Machine Learning
Causal machine learning is an increasingly important, but not well understood, technology. It’s a necessary precursor to building more human-like machine intelligence, and an integral factor in the fields of information, data and computer science. This track focuses on emerging causal machine learning technologies and the opportunities for practical impact at the intersection of academia and industry, with contributions from researchers at Microsoft and the broader academic and industrial research communities.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- Causal Machine Learning
- Date:
- Speakers:
- Cheng Zhang
- Affiliation:
- Microsoft Research
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Cheng Zhang
Principal Researcher
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Causal Machine Learning
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Opening remarks: Causal Machine Learning
Speakers:- Cheng Zhang
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Research talk: Causal ML and business
Speakers:- Jacob LaRiviere
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Research talk: Can causal learning improve the privacy of ML models?
Speakers:- Shruti Tople
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Panel: Challenges and opportunities of causality
Speakers:- Eric Horvitz,
- Yoshua Bengio,
- Susan Athey
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Research talk: Causal ML and fairness
Speakers:- Allison Koenecke
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Panel: Causal ML Research at Microsoft
Speakers:- Adith Swaminathan,
- Javier González Hernández,
- Justin Ding
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Research talk: Post-contextual-bandit inference
Speakers:- Nathan Kallus
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Demo: Enabling end-to-end causal inference at scale
Speakers:- Eleanor Dillon,
- Amit Sharma
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Panel: Causal ML in industry
Speakers:- Ya Xu,
- Totte Harinen,
- Dawen Liang
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Closing remarks: Causal Machine Learning
Speakers:- Emre Kiciman
