Research talk: Causal ML and business
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)
- Track:
- Causal Machine Learning
- Date:
- Speakers:
- Jacob LaRiviere
- Affiliation:
- Microsoft Research
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
