Closing remarks: Causal Machine Learning
- Emre Kiciman | Microsoft Research
- Microsoft Research Summit 2021 | 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)
Causal Machine Learning
-
Opening remarks: Causal Machine Learning
- Cheng Zhang
-
Research talk: Challenges and opportunities in causal machine learning
- Amit Sharma,
- Cheng Zhang,
- Emre Kiciman
-
Research talk: Causal ML and business
- Jacob LaRiviere
-
-
-
-
Research talk: Causality for medical image analysis
- Daniel Coelho de Castro
-
Research talk: Causal ML and fairness
- Allison Koenecke
-
Panel: Causal ML Research at Microsoft
- Adith Swaminathan,
- Javier González Hernández,
- Justin Ding
-
Research talk: Post-contextual-bandit inference
- Nathan Kallus
-
-
Demo: Enabling end-to-end causal inference at scale
- Eleanor Dillon,
- Amit Sharma
-
Panel: Causal ML at Microsoft
- Juan Lavista Ferres,
- Mingqi Wu,
- Sonia Jaffe
-
Panel: Causal ML in industry
- Greg Lewis,
- Ya Xu,
- Totte Harinen
-
Closing remarks: Causal Machine Learning
- Emre Kiciman
