An Empirical Investigation of InstanceSpecific Causal Bayesian Network Learning Significant progress has been made in developing algorithms for learning graphical causal models from data. Most of these algorithms learn a causal structure that is shared by all the instances (e.g., patients) in the training dataset. However, different instances may not all share the same causal structure. We introduced an instancespecific method called IGES 15 that learns a causal model for each instance  T  by using the features of  T  and the instances in the training dataset. In the current paper, we study the empirical performance of the IGES method on several biomedical datasets. The results provide support that instancespecific structure exists and is important to model in these real domains.

Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a realworld setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that oneu0027s choice of which to use should depend on the sample size.