Adaptation and Evidence-grounding of Generative Interventional Systems (AEGIS)
TL;DR
This workshop, held during ACM KDD 2026 conference, convenes researchers and practitioners in causality, LLMs, and prescriptive analytics to address when and how generative systems should recommend actions in healthcare and public policy.
Abstract
AEGIS advances generative AI that is fit for decision-making by grounding models in interventions, counterfactuals, and calibrated uncertainty. The workshop, held during ACM KDD 2026 conference, convenes researchers and practitioners in causality, LLMs, and prescriptive analytics to address when and how generative systems should recommend actions in healthcare and public policy. We invite methods that couple structural causal reasoning with LLMs, diffusion and sequence models; techniques for off-policy evaluation, dynamic treatment regimes, and feedback-aware learning; semi-synthetic benchmarks and governance practices; and measures beyond predictive fidelity, including policy regret, counterfactual calibration, and safety constraints. The program will feature a keynote talk and peer-reviewed presentations. By aligning with KDD's emphasis on trustworthy, scalable AI, AEGIS endeavors to establish shared evaluation protocols and artifacts that make prescriptive models reliable under distribution shift, so recommendations remain robust and accountable from development to deployment across settings and populations.