2023年2月27日 · Causal Machine Learning seems to be the most trending new buzzword in Data Science at the moment. But what is it really? In this blog series, we give a gentle introduction for newcomers to...

2024年4月19日 · Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.

2022年6月30日 · We categorize work in CausalML into five groups according to the problems they address: (1) causal supervised learning, (2) causal generative modeling, (3) causal explanations, (4) causal fairness, and (5) causal reinforcement learning.

The book introduces ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and presents Debiased Machine Learning methods to do inference in …

Causal machine learning is a branch of machine learning that focuses on understanding the cause and effect relationships in data. It goes beyond just predicting outcomes based on patterns in the data, and tries to understand how changing one variable can affect an outcome.

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.

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.