David Jensen

About

David Jensen is a leading researcher in artificial intelligence, with a primary focus on causal inference and relational machine learning. His work addresses fundamental challenges in learning causal models from data, which has broad implications for complex reasoning, planning, knowledge-base construction, and algorithmic fairness. Jensen’s influential paper, “The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data” (2019, 19 citations), advocates for rigorous evaluation standards in causal AI, pushing the field toward more reliable and actionable models. Earlier, his work on “A Relational Representation for Procedural Task Knowledge” (2005, 17 citations) introduced relational dependency networks to learn joint probability estimates from sensorimotor features, enabling agents to select actions most likely to succeed. This foundational contribution bridges relational learning and decision-making. Jensen’s research has shaped how AI systems reason about cause and effect in structured domains, earning him recognition as a key figure in advancing both the theory and application of causal and relational AI. His work continues to influence students and researchers tackling problems in robotics, explanation, and fairness.

Research Focus

Key Achievements

Top Papers

1. 1
   The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

   19 citations · 2019
2. 2
   A relational representation for procedural task knowledge

   17 citations · 2005

Key Collaborators

Contact & Links