# Information Bottleneck

The work introduces General Reward Inference and Disentanglement (GRID), a social learning method that uses an information bottleneck to decompose each agent's reward function into a general reward shared across all agents and specific rewards capturing individual preferences, so that training solely on the general reward yields a generalist agent internalizing universal environmental competencies such as safety and basic task proficiency; experiments on a synthetic basis function decomposition, multi-agent Craftax, MuJoCo Gym continuous control, and the Highway-Env driving simulator confirm semantically meaningful reward disentanglement, outperformance of standard learning-from-demonstration baselines, and more efficient and stable specialization.

The work introduces General Reward Inference and Disentanglement (GRID), a social learning method that uses an information bottleneck to decompose each agent's reward function into a general reward shared across all agents and specific rewards capturing individual preferences, so that training solely on the general reward yields a generalist agent internalizing universal environmental competencies such as safety and basic task proficiency; experiments on a synthetic basis function decomposition, multi-agent Craftax, MuJoCo Gym continuous control, and the Highway-Env driving simulator confirm semantically meaningful reward disentanglement, outperformance of standard learning-from-demonstration baselines, and more efficient and stable specialization.

This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn

This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn