LiNGAM - Discovery of non-gaussian linear causal models

Several authors (Spirtes et al. 2000; Pearl 2000) have recently formalized concepts related to causality using probability distributions defined on directed acyclic graphs. This line of research emphasizes the importance of understanding the process which generated the data, rather than only characterizing the joint distribution of the observed variables. The reasoning is that a causal understanding of the data is essential to be able to predict the consequences of interventions, such as setting a given variable to some specified value.

One of the main questions one can answer using this kind of theoretical framework is: 'Under what circumstances and in what way can one determine causal structure on the basis of observational data alone?'. In many cases it is impossible or too expensive to perform controlled experiments, and hence methods for discovering likely causal relations from uncontrolled data would be very valuable.

We have developed a method, abbreviated LiNGAM, for identifying Linear, Non-Gaussian, Acyclic causal Models based on purely observational data. This method can be seen as an extension of the standard SEM (Structural Equation Model; see, for instance, Bollen 1989) framework. The key aspect of our method is that when the data fulfills the linearity and non-gaussianity conditions, and there are no unobserved confounders, one can identify the full causal structure without any prior information on the time-ordering of the variables.

For more information, including relevant publications as well as the full code package, see the LiNGAM home page.