Xinwei Shen's scientific contributions

Publications (4)

Preprint
In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical representation of the Markov equivalence class (MEC). In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorit...
Conference Paper
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessaril...
Preprint
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally correlated. We show that previous methods with independent priors fail to disent...
Preprint
Learning disentanglement aims at finding a low dimensional representation, which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder is commonly used to disentangle independent factors from observations. However, in real scenarios, the factors with semantic meanings are not nec...

Citations

... It is therefore natural to generalize conceptual distinctness to variables that are causally autonomous; i.e., interventions can be performed on each variable separately. This motivates causal disentanglement [Yang et al., 2021], the recovery of causally autonomous variables from data. ...