Traditional data science approaches rely on correlation, which lead to black boxes that humans cannot understand. By applying causal techniques to machine learning, we can build AI systems that are more accurate, transparent, and fair.

Research

We do research on fundamental solutions to real-world AI ethics problems. By working at the intersection of machine learning, causal inference, and systems thinking, we can identify the root causes of AI bias and prevent models from causing illegal discrimination.

Technology

We develop causal AI technology that can help companies gain a strategic advantage over their competition. Our patented AI technology may help companies leverage far more data than their competitors, while providing stronger mathematical guardrails around AI systems.

Policy

We provide non-partisan advice to policy makers on both sides of the aisle​ for how to modernize AI regulations so that they are aligned to society's values. We take a mathematical approach to regulation, focusing on solutions that can actually be implemented in practice.

Education

We publish our research to help advance the field of data science. We partner with universities to train the next generation of data scientists, who will be able to combine knowledge from the social sciences with computer science to develop sociotechnical solutions to AI problems.​