Research talk: Causal ML and business
- Jacob LaRiviere | Microsoft Research
- Microsoft Research Summit 2021 | Causal Machine Learning
Using machine learning for causal inference can, in a subset of cases with rich data, replicate results from A/B experimentation. For other cases, like identifying the “average treatment effect for compliers” ML offers more limited scope. There is room to progress methodologically on this front. In the best-case scenario, where we get the tools to estimate average treatment effects for compliers, there is a straightforward path to get a scalable inference service off the ground, similar to experimental platforms. In this session, Microsoft economics researcher Jacob LaRiviere will discuss some experiences with causal ML in business scenarios.
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
