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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…
Less Greedy Equivalence Search
Adiba Ejaz, Elias Bareinboim
Greedy Equivalence Search (GES) is a classic score-based algorithm for causal discovery from observational data. In the sample limit, it recovers the Markov equivalence class of gr…
From Black-box to Causal-box: Towards Building More Interpretable Models
Inwoo Hwang, Yushu Pan, Elias Bareinboim
Understanding the predictions made by deep learning models remains a central challenge, especially in high-stakes applications. A promising approach is to equip models with the abi…
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…
Partial Identification Approach to Counterfactual Fairness Assessment
Saeyoung Rho, Junzhe Zhang, Elias Bareinboim
The wide adoption of AI decision-making systems in critical domains such as criminal justice, loan approval, and hiring processes has heightened concerns about algorithmic fairness…
Causal Abstraction Inference under Lossy Representations
Kevin Xia, Elias Bareinboim
The study of causal abstractions bridges two integral components of human intelligence: the ability to determine cause and effect, and the ability to interpret complex patterns int…