Robert Osazuwa Ness

关于

Robert Osazuwa Ness is a Senior Researcher at Microsoft Research AI n the AI Interaction and Learning group. His research centers on causal reasoning, with a particular focus on how causal structure can improve the learning, evaluation, and control of modern AI systems. He works at the intersection of causal inference, probabilistic modeling, and generative AI, developing methods that make complex models more interpretable, reliable, and scientifically grounded.

A central theme of his work is bringing causal ideas into generative modeling. More broadly, Ness develops statistical methods for the training, evaluation, and control of agent models. His goal is to connect rigorous methodology with practical engineering tools for building more dependable AI systems.

Research

Ness’s research focuses on two closely connected areas:

Causal reasoning and world modeling.

He studies the causal assumptions and inductive biases needed for AI systems to reason about interventions, mechanisms, and counterfactuals. His work explores how these ideas can be incorporated into multimodal and generative models, including diffusion-based world models, through architecture, learning objectives, fine-tuning, and prompting. Recent work here includes:

• Multiverse Mechanica (opens in new tab): A Testbed for Learning Game Mechanics via Counterfactual Worlds (with Ricardo Cannizzaro, Yunshu Wu, and Lars Kunze), accepted to ICLR 2026. This explores how interactive environments can serve as testbeds for learning game mechanics through counterfactual world models.
• See our seminal paper on causal reasoning in LLMs –  Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Statistical methods for agent modeling.

He develops methods for the training, evaluation, and control of agent models and language-based systems. This includes experimental design for complex model pipelines such as GraphRAG, nonparametric methods for validating adversarial attacks such as MedFuzz, and Bayesian partial pooling (opens in new tab) methods for analyzing benchmark behavior and model unfaithfulness.

Other Work

I am author of a book called Cau (opens in new tab)sal AI (opens in new tab), the result of a passion-project to unite graphical causal inference with deep generative AI. Here are some testimonials:

(opens in new tab)Causal AI is a timely and comprehensive resource in meeting growing demands for AI systems that generate and understand causal narratives about our world. Robert Ness has done a fantastic job guiding the reader, step by step, from  the mathematical principles of causal science to real life applications, integrating ideas from reinforcement learning, generative AI and counterfactual logic. The code examples in this book are state-of-the-art, and will help readers ramp up quickly with many case studies and motivating applications. It’s exciting to have a learning resource to recommend that tightly integrates so many powerful ideas while remaining accessible and practical.

~ Judea Pearl, UCLA, author of Book of Why and Causality

There are many theoretical books on causality (by Pearl, Robins, et al), and a few practical books (eg  by Kolnar), but this book by Robert Ness combines the best of both worlds. He clearly explains the principles and assumptions behind topics such as counterfactual queries, while also giving examples of how to implement these ideas using various Python toolboxes (such as PgmPy, DoWhy, and Pyro). He goes beyond standard statistical textbooks by discussing topics of interest to people in machine learning, such as using deep neural networks, Bayesian causal models, and connections with RL and LLMs.

~ Kevin Murphy, Google AI, author of Probabilistic Machine Learning

AI, causal inference, and Bayesian modeling are at the forefront of modern data science, and this book expertly combines all three. The book covers a scalable workflow from basics to advanced applications, including a thorough treatment of Bayesian approaches to causal inference. Robert weaves ideas from statistics, machine learning, and causal inference into an accessible guide, with numerous business-relevant examples from tech, retail, and marketing. This text is invaluable for developing robust and explainable AI systems grounded in causal thinking, with clear applications to real-world business problems.

~ Thomas Wiecki, Founder of PyMC Labs, Core Developer for PyMC

Integrating causality into AI crucially breaks through the ‘black box’ barrier to interpretability, resulting in models that are more robust and capable of reasoning, explaining, and adapting. Robert Osazuwa Ness demystifies causal AI with a code-first, hands-on approach, using accessible tools like PyTorch and DoWhy, and breaking down complex concepts into implementable, digestible steps. Causal AI serves as both a textbook and a reference, and is written in an engaging, conversational style that clarifies concepts and welcomes newcomers, while spanning advanced content for seasoned users. As data scientists, machine learning researchers, and tech innovators, we will benefit by being able to move beyond correlation, harness domain knowledge, and build intelligent systems grounded in causality. I’m excited to share this book with students and colleagues, and watch it shift the conversation from mere prediction to the power of reasoning in AI.

~ Karen Sachs, Founder of Next Generation Analytics and Aeon Bio

In a research collaboration with Madeleine Daepp, we study how generative AI is changing political messaging in democracies. This work was featured in The Economist (opens in new tab).

(opens in new tab)

I received my Ph.D. in statistics from Purdue University, where my dissertation focused on Bayesian active learning for causal discovery. I’m a Johns Hopkins SAIS alumni and a graduate of the Hopkins-Nanjing Center.