Causality and Machine Learning Research at Microsoft

Causality and Machine Learning at Microsoft Research

This page from Microsoft Research provides an in-depth look into their work on Causality and Machine Learning. It serves as a central hub for understanding the group's research focus, key personnel, publications, and recent activities.

Research Areas and Focus

The content outlines the various domains within Microsoft Research, including:

• Intelligence: Artificial Intelligence, Audio & Acoustics, Computer Vision, Graphics & Multimedia, Human-Computer Interaction, Human Language Technologies, Information Retrieval.
• Systems: Data Platforms & Analytics, Hardware & Devices, Programming Languages & Software Engineering, Quantum Computing, Security, Privacy & Cryptography, Systems & Networking.
• Theory: Algorithms, Mathematics.
• Other Sciences: Ecology & Environment, Economics, Medical, Health & Genomics, Social Sciences, Technology for Emerging Markets.

Key Initiatives and News

The "News and Features" section highlights recent developments and contributions:

• Research Focus: Week of April 21, 2025: This upcoming feature will cover contributions to CHI 2025 & ICLR 2025, research on causal reasoning and LLMs, methods for countering LLM jailbreak attacks, comparisons of human vs. AI-alone approaches, and insights into rural healthcare innovation from Jim Weinstein.
• Podcasts:
  • "What’s Your Story: Emre Kiciman": Discusses Emre Kiciman's transition from systems and networking to computational social science and causal analysis, emphasizing the impact of systems thinking.
  • "AI Frontiers: The future of causal reasoning with Emre Kiciman and Amit Sharma": Features a discussion on the causal capabilities of Large Language Models (LLMs) and ongoing work with GPT-3.5 and GPT-4.
• Blog Posts:
  • "Microsoft is teaching computers to understand cause and effect" (TechRepublic): Explores how causal machine learning and Microsoft's next-best-question model can improve business decision-making by replacing A/B testing.
  • "DoWhy evolves to independent PyWhy model to help causal inference grow": Details the evolution of the DoWhy library into the PyWhy model, facilitating the growth of causal inference research and its application in understanding cause-and-effect relationships.
  • "Adversarial machine learning and instrumental variables for flexible causal modeling": Discusses the shift towards using ML for automated decision-making in various fields, such as personalized medicine and marketing.
  • "Open-source library provides explanation for machine learning through diverse counterfactuals": Introduces a library that offers explanations for ML decisions using counterfactuals, helping to understand why an application (e.g., a loan) was rejected.
• Past Research Focuses: Mentions of previous "Research Focus" posts highlighting contributions to conferences like NeurIPS 2022 and work on machine translation.

Navigation and Resources

The page also provides extensive navigation to:

• Products and Downloads: Tools and resources for researchers and developers.
• Programs and Events: Academic programs, conferences, and events.
• Careers: Opportunities within Microsoft Research.
• People: Index of researchers and emeritus programs.
• Blogs and Learning: Access to the Microsoft Research Blog, podcast, webinars, and newsletter.
• Labs and Locations: Information on various Microsoft Research labs worldwide (AI4Science, Asia, Cambridge, India, Montreal, New York City, Redmond, etc.) and specialized labs like Applied Sciences Group and Mixed Reality labs.
• Everything Microsoft: Links to global Microsoft products, cloud services, AI initiatives, industry solutions (Education, Automotive, Financial Services, Healthcare, etc.), partner programs, and resources like Microsoft Learn and documentation.

Social Engagement

Links are provided to follow Microsoft Research on X, Facebook, LinkedIn, YouTube, and Instagram, as well as an RSS feed for updates. Sharing options for X, Facebook, LinkedIn, and Reddit are also available.

The content emphasizes Microsoft's commitment to advancing AI and machine learning, particularly in the area of causal inference, and making these advancements accessible through various platforms and resources.