97 citations · 563 across the 101 of their papers we have counts for
13 papers · 1 filter
Instrumental Variables in Causal Inference and Machine Learning: A Survey
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +1
Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. T…
Confounder Balancing for Instrumental Variable Regression with Latent Variable
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +2
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.…
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Furui Liu +1
Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of mult…
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
Leilei Gan, Baokui Li, Kun Kuang +5
Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term. A core problem of LJP is how to di…
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples
Chengyuan Liu, Leilei Gan, Kun Kuang +1
The aim of Logic2Text is to generate controllable and faithful texts conditioned on tables and logical forms, which not only requires a deep understanding of the tables and logical…
Domain Generalization via Contrastive Causal Learning
Qiaowei Miao, Junkun Yuan, Kun Kuang
Domain Generalization (DG) aims to learn a model that can generalize well to unseen target domains from a set of source domains. With the idea of invariant causal mechanism, a lot…