Estimating a Causal Order among Groups of Variables in Linear Models
This work addresses the problem of causal inference for multi-dimensional data in machine learning, representing an incremental advancement by extending scalar-based methods to vector-based scenarios.
The authors tackled the problem of inferring causal relationships from statistical data by generalizing existing methods to apply to collections of multi-dimensional random vectors in linear models, showing that their methods can provide useful information on causal relationships even for relatively small sample sizes in simulations.
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among scalar random variables. We generalize existing methods to apply to collections of multi-dimensional random vectors, focusing on techniques applicable to linear models. The performance of the resulting algorithms is evaluated and compared in simulations, which show that our methods can, in many cases, provide useful information on causal relationships even for relatively small sample sizes.