3 papers
stat.ML2022
Correcting Confounding via Random Selection of Background Variables
You-Lin Chen, Lenon Minorics, Dominik Janzing
We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of…
stat.ML2020
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang +3
Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration…
stat.ML2019
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees
You-Lin Chen, Mladen Kolar, Ruey S. Tsay
In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors…