Causality and Machine Learning
新闻与深度文章
What’s Your Story: Emre Kiciman
| Johannes Gehrke 和 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…
| Ashley Llorens, Emre Kiciman, 和 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.”
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…
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,…
新闻报道 | 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…
| Emre Kiciman 和 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…
| 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…
| Adith Swaminathan 和 Emre Kiciman
Causal inference studies the relationship between causes and effects. For example, one kind of question that causal inference can answer is the “What-happens-if …” question. What happens if I take a specific medication? What happens if I raise the price…