Mixed Graphical Models for Causal Analysis of Multi-modal Variables
This work addresses a bottleneck in causal analysis for biological researchers by enabling more accurate and efficient modeling of mixed data types, though it is incremental as it builds on existing methods.
The paper tackled the problem of learning sparse directed causal graphs from multi-modal biological datasets with mixed variable types, by developing hybrid methods that combine undirected and directed graph learning approaches, resulting in faster and better performance than directed-only methods across various parameter settings and dataset sizes.
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data.