6 citations · 8 across the 13 of their papers we have counts for
13 papers
Score-Based Causal Discovery of Latent Variable Causal Models
Ignavier Ng, Xinshuai Dong, Haoyue Dai +3
Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under the category of constraint-…
The Power of Order: Fooling LLMs with Adversarial Table Permutations
Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5
Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, the…
Causal Representation Learning from General Environments under Nonparametric Mixing
Ignavier Ng, Shaoan Xie, Xinshuai Dong +2
Causal representation learning aims to recover the latent causal variables and their causal relations, typically represented by directed acyclic graphs (DAGs), from low-level obser…
Latent Variable Causal Discovery under Selection Bias
Haoyue Dai, Yiwen Qiu, Ignavier Ng +3
Addressing selection bias in latent variable causal discovery is important yet underexplored, largely due to a lack of suitable statistical tools: While various tools beyond basic…
Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Haoyue Dai +4
Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solv…
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypotheti…
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious atta…
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This…
When Selection Meets Intervention: Additional Complexities in Causal Discovery
Haoyue Dai, Ignavier Ng, Jianle Sun +5
We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug…
On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Biwei Huang +5
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, we examine the paramet…
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun +4
Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rel…
Prompting Fairness: Integrating Causality to Debias Large Language Models
Jingling Li, Zeyu Tang, Xiaoyu Liu +4
Large language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes d…
Procedural Fairness Through Decoupling Objectionable Data Generating Components
Zeyu Tang, Jialu Wang, Yang Liu +2
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (…