Research talk: Causal ML and fairness
- Allison Koenecke | Microsoft Research
- Microsoft Research Summit 2021 | Causal Machine Learning
Observing heterogeneous treatment effects across different demographic groups is an important mechanism for evaluating fairness. However, relatively little data is available for certain demographics, in which case researchers may combine multiple data sources to increase statistical power. The stakes are especially high in healthcare—it is imperative to accurately measure the effectiveness of treatments for diseases that could disproportionately impact underrepresented patient subgroups. Join researcher Allison Koenecke, from the Machine Learning & Statistics Group at Microsoft Research New England, to discuss federated causal inference. Because legal and privacy considerations may restrict individual-level information sharing across data sets, we introduce federated methods for treatment effect estimation that only utilize summary-level statistics from each data set. These asymptotically guaranteed methods provide variance estimates and doubly robust treatment effects under model assumptions on heterogeneous data sets.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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Allison Koenecke
Postdoctoral Researcher
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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
