Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning
Causal relationships are stable across distribution shifts. Models based on causal knowledge have the potential to generalize to unseen domains and offer counterfactual predictions: how do outcomes change if a certain feature is changed in the real world. In recent years, machine learning methods based on causal reasoning have led to advances in out-of-domain generalization, fairness and explanation, and robustness to data selection biases. ¬ In this session, we discuss big ideas at the intersections of causal inference and machine learning towards building stable predictive models and discovering causal insights from data.
Special MSR India session
Session Lead: Amit Sharma, Microsoft
Speaker: Susan Athey, Stanford University
Talk Title: Causal Inference, Consumer Choice, and the Value of Data
Speaker: Elias Bareinboim, Columbia University
Talk Title: On the Causal Foundations of Artificial Intelligence (Explainability & Decision-Making)
Speaker: Cheng Zhang, Microsoft
Talk Title: A causal view on Robustness of Neural Networks
Q&A panel with all 3 speakers
- Date:
- Haut-parleurs:
- Amit Sharma, Susan Athey, Elias Bareinboim, Cheng Zhang,
- Affiliation:
- Microsoft Research, Stanford University, Columbia University, Microsoft Research
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Amit Sharma
Principal Researcher
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Cheng Zhang
Principal Researcher
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