Extensions of ICA for Causality Discovery in the Hong Kong Stock Market

By Kun Zhang, L. Chan. 2006

46 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Kun Zhang
2. Clark Glymour
3. Peter Spirtes
4. Biwei Huang
5. J. Ramsey
6. Bernhard Schölkopf
7. Eric P. Xing
8. Mingming Gong

In Artificial Intelligence

1. Bernhard Schölkopf
2. Kun Zhang
3. Jan Peters
4. Peter Spirtes
5. Clark Glymour
6. Biwei Huang
7. Jiji Zhang
8. Eric P. Xing

In Signal Processing

1. Bernhard Schölkopf
2. Kun Zhang
3. Clark Glymour
4. Jiji Zhang
5. Peter Spirtes
6. Biwei Huang
7. Eric P. Xing
8. J. Ramsey

Citations in the corpus

• Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “, 2009; Zhang and Hyvärinen, 2009a), or the post-nonlinear model (Zhang and Chan, 2006; Zhang and Hyvärinen, 2009b).”
• Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, background): “, 2009; Zhang and Hyvärinen, 2009a], or the post-nonlinear causal model [Zhang and Chan, 2006; Zhang and Hyvärinen, 2009b]) to distribution-shifted data, the estimated noise may not be independent from the cause any more.”
• Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results (2018, methodology): “The measuring process may involve nonlinear distortion, as already address by the post-nonlinear causal model (Zhang & Hyvärinen, 2009; Zhang & Chan, 2006), and may introduce a lot of error.”
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality (2023, background): “In some specific settings, such as adding non-gaussian noise to linear data-generating process (Shimizu et al., 2006) or assuming that all functions are non-linear with additive noise (Zhang & Chan, 2006; Zhang & Hyv ¨ arinen, 2009), identification is possible.”
• Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019, background): “Under this assumption, causal relations can be identified by leveraging the conditional independence between observed variables [33] or the asymmetrical independence between estimated noise term and hypothetical causes, implied by suitable functional causal models [31, 37, 14, 39].”
• Review of Causal Discovery Methods Based on Graphical Models (2019, background): “In the post-nonlinear (PNL) causal model (Zhang and Chan, 2006; Zhang and Hyvärinen, 2009b), the effect Y is further generated by a post-nonlinear transformation on the non-linear effect of the cause X plus noise term ε : where both f 1 and f 2 are non-linear functions and f 2 is assumed to be…”