Keyword Analysis & Research: causality in machine learning


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Frequently Asked Questions

What is causal machine learning?

Guided by joint formal reasoning over observations and auxiliary information about data collection procedures or other domain knowledge, causal machine learning methods are grounded in the stable and independent mechanisms that govern the behavior of a system being modelled.

What can we learn from causality research?

At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying all these methods for data-driven decision making in real-world contexts.

Can machine learning learn about cause-effect relationships?

Indeed, ML systems excel in learning connections between input data and output predictions, but lack in reasoning about cause-effect relations or environment changes. ML models that could capture causal relationships will be more generalizable.

Can AI use causal inference and machine learning to measure effects?

Here, we discuss how AI can use causal inference and machine learning to measure the effects of multiple variables – and why it’s important for technological progression. In a major operator’s network control center complaints are flooding in.


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