Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make the assumption of additive noise. Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships. The proposed method is based on recent progress in non-linear independent component analysis and exploits the non-stationarity of observations in order to recover the underlying sources or latent disturbances. We show rigorously that in the case of bivariate causal discovery, such non-linear ICA can be used to infer the causal direction via a series of independence tests. We further propose an alternative measure of causal direction based on asymptotic approximations to the likelihood ratio, as well as an extension to multivariate causal discovery. We demonstrate the capabilities of the proposed method via a series of simulation studies and conclude with an application to neuroimaging data.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Nonlinear causal discovery with additive noise models
The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models are often used because these models are well understo…
Causal DiscoveryDiscovering Mixtures of Structural Causal Models from Time Series Data
Discovering causal relationships from time series data is significant in fields such as finance, climate science, and neuroscience. However, contemporary techniques rely on the simplifying assumption that data originates…
Causal DiscoveryTime SeriesVariational InferenceFCause: Flow-based Causal Discovery
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values. This limits their reliability and applicability. We propose FCause, a new flow…
Causal DiscoveryMissing ValuesNeural Additive Vector Autoregression Models for Causal Discovery in Time Series
Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time serie…
Causal DiscoveryTime SeriesTime Series AnalysisMDL Meets Latent Confounders: LNML-based Causal Discovery
Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting their applicability. We propose an MDL-based…