Abstract
Causal discovery is an increasingly important method for data analysis in the field of medical research. In this paper, we consider two challenges in causal discovery that occur very often when working with medical data: a mixture of discrete and continuous variables and a substantial amount of missing values. To the best of our knowledge, there are no methods that can handle both challenges at the same time. In this paper, we develop a new method that can handle these challenges based on the assumption that data are missing at random and that continuous variables obey a non-paranormal distribution. We demonstrate the validity of our approach for causal discovery on simulated data as well as on two real-world data sets from a monetary incentive delay task and a reversal learning task. Our results help in the understanding of the etiology of attention-deficit/hyperactivity disorder (ADHD).
Highlights
In recent years, the use of causal discovery in the field of medical research has become increasingly popular
We considered the Waste Incinerator Network when the correlation between variables is extreme-high and medium
The simulation study shows that the expectation maximization (EM) algorithm performs better than Spearman with pairwise correlation, mean imputation, and list-wise deletion for directed graphical models when the percentage of missing values is high, while providing similar results when the percentage is low
Summary
The use of causal discovery in the field of medical research has become increasingly popular. [1,34,53], the authors propose different methods to estimate the correlation matrix for data with missing values and mixture variables, and based on this correlation matrix learn the structure of the undirected graphical model. Even though the methods that are considered in this paper to estimate correlation matrices have similar performance for the undirected graphical model, our analysis suggests that these methods have a different effect on the accuracy of a causal discovery algorithm. The second data set studies how problems with learning from reinforcement are associated with ADHD symptoms using a probabilistic reversal learning task (PRL) Based on this data, we build two causal models that provide deeper understanding of the altered reward processing and reversal learning in adolescents with ADHD than standard statistical tests.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
