25 papers
Scalable Causal Imitation Learning
Eylam Tagor, Mingxuan Li, Elias Bareinboim
Imitation learning enables learning a policy in an unknown environment with a latent reward signal using expert demonstrations, but it struggles when the imitator's and expert's ob…
Counterfactual Shapley Credit Assignment
Mingxuan Li, Kai-Zhan Lee, Kaizhan-Lee +1
The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing frameworks, whether relying on temporal cont…
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…
Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding
Junzhe Zhang, Jingyuan Chen, Elias Bareinboim
The presence of confounding bias poses a key challenge in policy evaluation, as the target causal effects of actions are not identifiable (i.e., underdetermined) from observational…
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…
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…