8 papers · 1 filter
An Introduction to Causal Reinforcement Learning
Elias Bareinboim, Junzhe Zhang, Sanghack Lee
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.e., w…
Relational Structural Causal Models
Adiba Ejaz, Elias Bareinboim
An artificial intelligence must have a model of its environment that is causal, supporting reasoning about interventions and counterfactuals, and also combinatorial, supporting gen…
Causal Algorithmic Recourse: Foundations and Methods
Drago Plecko, Collin Wang, Elias Bareinboim
The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations for how an individual may rever…
Causal Identification from Counterfactual Data: Completeness and Bounding Results
Arvind Raghavan, Elias Bareinboim
Previous work establishing completeness results for counterfactual identification has been circumscribed to the setting where the input data belongs to observational or interventio…
Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge
Drago Plecko, Patrik Okanovic, Shreyas Havaldar +2
Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable…
Confounding Robust Deep Reinforcement Learning: A Causal Approach
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, l…