Demo: Enabling end-to-end causal inference at scale
- Eleanor Dillon, Amit Sharma | Microsoft Research
- Microsoft Research Summit 2021 | Causal Machine Learning
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)
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
