Causality and Machine Learning
뉴스 & 기능
Research Focus: Week of April 21, 2025
In this issue: our CHI 2025 & ICLR 2025 contributions, plus research on causal reasoning & LLMs; countering LLM jailbreak attacks; and how people use AI vs. AI-alone. Also, SVP of Microsoft Health Jim Weinstein talks rural healthcare innovation.
What’s Your Story: Emre Kiciman
| Johannes Gehrke 그리고 Emre Kiciman
Emre Kiciman shares how some keen observations and a desire to have front-end impact led him to make the jump from systems and networking to computational social science and now causal analysis and large-scale AI—and how systems thinking still impacts…
AI Frontiers: The future of causal reasoning with Emre Kiciman and Amit Sharma
| Ashley Llorens, Emre Kiciman, 그리고 Amit Sharma
Emre Kiciman and Amit Sharma join Ashley Llorens to discuss the causal capabilities of LLMs and ongoing journeys with GPT-3.5 and GPT-4 in the newest episode of the Microsoft Research Podcast series, “AI Frontiers.”
Research Focus: Week of March 27, 2023
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Machine translation (MT) models are designed to learn from large amounts of data…
Research Focus: Week of November 28, 2022
This special edition of Research Focus highlights some of the 100+ papers from Microsoft Research that were accepted for publication at NeurIPS 2022 – the thirty-sixth annual Conference on Neural Information Processing Systems. Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey,…
뉴스에서 | TechRepublic
Microsoft is teaching computers to understand cause and effect
Causal machine learning with Microsoft’s next-best-question model could replace AB testing to help you make better business decisions. AI that analyzes data to help you make decisions is set to be an increasingly big part of business tools, and the…
DoWhy evolves to independent PyWhy model to help causal inference grow
| Emre Kiciman 그리고 Amit Sharma
Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven…
Adversarial machine learning and instrumental variables for flexible causal modeling
| Vasilis Syrgkanis
We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of domains: what personalized treatment should be administered to a patient, what discount should be offered…
Open-source library provides explanation for machine learning through diverse counterfactuals
| Amit Sharma
Consider a person who applies for a loan with a financial company, but their application is rejected by a machine learning algorithm used to determine who receives a loan from the company. How would you explain the decision made by…