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20122022
most citedKernel-based Conditional Independence Test and Application in Causal Discovery

351 citations · 1.2k across the 48 of their papers we have counts for

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33 papers · 1 filter

cs.LG2022★ 6 cited

A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes

Wenzhuo Yang, Kun Zhang, Steven C. H. Hoi

Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system…

cs.LG2022★ 4 cited

Causal Balancing for Domain Generalization

Xinyi Wang, Michael Saxon, Jiachen Li +3

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerabi…

cs.LG2022★ 3 cited

Counterfactual Fairness with Partially Known Causal Graph

Aoqi Zuo, Susan Wei, Tongliang Liu +3

Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair mach…

cs.LG2022★ 11 cited

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Erdun Gao, Ignavier Ng, Mingming Gong +5

State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as…

cs.LG2022★ 4 cited

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

Ignavier Ng, Yujia Zheng, Jiji Zhang +1

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness. However, the assumption can be approximately violated in many ways, le…

cs.LG2022★ 1 cited

Optimal transport for causal discovery

Ruibo Tu, Kun Zhang, Hedvig Kjellström +1

To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the…