Demo: Enabling end-to-end causal inference at scale
This session will present the two popular open-source tools for causal inference, DoWhy and EconML, developed by Microsoft Research. In this demo, researchers Amit Sharma and Eleanor Dillon will describe how the integrated toolkit (DoWhy+EconML) provides an end-to-end API for causal inference, access to state-of-the-art effect estimation algorithms, and methods to evaluate the validity of a causal estimate.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- Causal Machine Learning
- Date:
- Speakers:
- Eleanor Dillon, Amit Sharma
- 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
