University of Massachusetts Amherst: CICS’ Shlomo Zilberstein and Multi-Institutional Colleagues Secure $5 Million NSF Grant to Enhance Causal Decision Making
Shlomo Zilberstein, professor in the Manning College of Information and Computer Sciences (CICS), is one of five co-principal investigators for a $5 million, multi-institutional National Science Foundation (NSF) grant to improve AI-based causal decision making. The four-year grant will support “Causal Foundations of Decision Making and Learning,” a collaborative project led by Elias Bareinboim of Columbia University and co-principal investigators Rina Dechter of the University of California Irvine, Sven Koenig of the University of Southern California, and Jin Tian of Iowa State University, in partnership with ACM A.M. Turing Award winner Judea Pearl of the University of California Los Angeles.
As AI becomes increasingly integrated with daily life, automated systems are entrusted with making some of the decisions traditionally left in the hands of humans. Much like a detective analyzes clues to solve cases, the current crop of AI systems is heavily reliant on data, using statistical methods to make sense of it; however, relying solely on statistical associations has limitations. Current methods utilized in AI decision-making, such as model-based planning and model-free reinforcement learning, fall short of explicitly addressing causal mechanisms like environment changes. While these methods are excellent at working with patterns and statistical associations, they lack the depth needed to understand the intricate cause-and-effect relationships of an ever-changing world.
“Many of the hardest challenges for AI systems could be better addressed with causal reasoning. For instance, a medical diagnostic system could establish a causal relationship between a proposed treatment and an outcome, whereas today’s methods can only determine a correlation,” says Zilberstein. “Causality offers a far better foundation for verification and explanation of automated decisions.”
The project will combine the framework of structural causal models (SCM) with the leading approaches for decision-making in AI–including model-based planning, reinforcement learning, and graphical models such as influence diagrams–to create a new perspective, envisioning AI systems as existing within a world that’s treated as its own SCM. The team also plans to design a unique architecture that clearly distinguishes the system model from the world model. This separation would allow them to articulate precisely what the system knows, like a causal graph – and what it doesn’t know, such as confounding factors or unknown causal mechanisms. The goal is to create new foundations, including principles, methods, and tools for systems that make decisions based on causal relationships.
Zilberstein’s efforts will primarily focus on the scalability and efficiency of the tools developed, including addressing the tradeoff between multiple objectives, balancing explainability and decision quality, and tackling the challenge of learning causal models of the world.
“Optimizing decisions remains difficult because there is never complete and perfect information to inform these decisions. Managing the tradeoffs that arise is key to success and scalability. My lab is developing new methods that allow users to better understand and control these tradeoffs and provide better input to guide the AI system,” he says.