5 citations · 12 across the 11 of their papers we have counts for
11 papers
Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data ge…
Error Probability Bounds for Invariant Causal Prediction via Multiple Access Channels
Austin Goddard, Yu Xiang, Ilya Soloveychik
We consider the problem of lower bounding the error probability under the invariant causal prediction (ICP) framework. To this end, we examine and draw connections between ICP and…
Identifying Direct Causes using Intervened Target Variable
Kang Du, Yu Xiang, Ilya Soloveychik
Identifying the direct causes or causal parents of a target variable is crucial for scientific discovery. Focusing on linear models, the invariant prediction framework was built up…
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Jie Qiao, Ruichu Cai, Siyu Wu +3
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes p…
Generalized Invariant Matching Property via LASSO
Kang Du, Yu Xiang
Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance p…
Learning Invariant Representations under General Interventions on the Response
Kang Du, Yu Xiang
It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors…
Lower Bounds on the Error Probability for Invariant Causal Prediction
Austin Goddard, Yu Xiang, Ilya Soloveychik
It is common practice to collect observations of feature and response pairs from different environments. A natural question is how to identify features that have consistent predict…
An Invariant Matching Property for Distribution Generalization under Intervened Response
Kang Du, Yu Xiang
The task of distribution generalization concerns making reliable prediction of a response in unseen environments. The structural causal models are shown to be useful to model distr…
Variable Selection with the Knockoffs: Composite Null Hypotheses
Mehrdad Pournaderi, Yu Xiang
The fixed-X knockoff filter is a flexible framework for variable selection with false discovery rate (FDR) control in linear models with arbitrary design matrices (of full column r…
A Subsampling-Based Method for Causal Discovery on Discrete Data
Austin Goddard, Yu Xiang
Inferring causal directions on discrete and categorical data is an important yet challenging problem. Even though the additive noise models (ANMs) approach can be adapted to the di…
Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data gen…