7 papers
Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models
Pengcheng Tan, Jiang Chen, Dehui Du
Structural causal models provide a unified semantics for interventions and counterfactuals, but most identifiability results rely on restrictive assumptions like global monotonicit…
Causal Discovery via Quantile Partial Effect
Yikang Chen, Xingzhe Sun, Dehui Du
Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that w…
Exogenous Isomorphism for Counterfactual Identifiability
Yikang Chen, Dehui Du
This paper investigates -identifiability, a form of complete counterfactual identifiability within the Pearl Causal Hierarchy (PCH) framework, ensuring that a…
Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
Yikang Chen, Dehui Du, Lili Tian
We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common…
CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning
Jingwen Wang, Dehui Du, Yida Li +2
In the training process of Deep Reinforcement Learning (DRL), agents require repetitive interactions with the environment. With an increase in training volume and model complexity,…
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Yuanhao Liu, Dehui Du, Zihan Jiang +2
Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions.…
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
Yanyun Wang, Dehui Du, Haibo Hu +2
Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC). However, neural networks (NNs) are vulnerable to adversarial s…