6 accepted papers
Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that when the QPE of cause on effect is assumed to lie in a finite linear span, cause and effect are identifiable from their o…
Deep reinforcement learning (DRL) has gained significant attention in autonomous systems, yet its black-box nature and lack of explainability hinder user trust in safety-critical domains such as autonomous driving. Existing experience replay approaches enhance sample efficiency but often fail to cap…
This paper investigates $\sim_{\mathcal{L}\_3}$-identifiability, a form of complete counterfactual identifiability within the Pearl Causal Hierarchy (PCH) framework, ensuring that all Structural Causal Models (SCMs) satisfying the given assumptions provide consistent answers to all causal questions.…
Imitation learning is a promising approach to acquiring autonomous driving policies by mimicking human driver behaviors. However, a major drawback of existing driving policies derived from imitation learning is their proneness to capturing spurious correlations, owing to the lack of an explicit caus
Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. Existing methods to improve LLMs’ logical capabilities either involve traceable or verifiable logical sequences that generate more reliable responses…
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance minimization problem into a conditional distribution…