We revisit the structure learning problem for dynamic Bayesian networks and propose a
method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter …

When domain knowledge is limited and experimentation is restricted by ethical, financial, or
time constraints, practitioners turn to observational causal discovery methods to recover the …

Many investigations into the world, including philosophical ones, aim to discover causal
knowledge, and many experimental methods have been developed to assist in causal …

This paper proposes a new method to empirically validate simulation models that generate
artificial time series data comparable with real-world data. The approach is based on …

We present constraint-based and (hybrid) score-based algorithms for causal structure
learning that estimate dynamic graphical models from multivariate time series data. In …

Physicians' participation in online healthcare platforms serves to integrate online healthcare
resources with the offline medical system. This integration brings opportunities for resha** …

We study the identification of direct and indirect causes on time series with latent variables,
and provide a constrained-based causal feature selection method, which we prove that is …

Cognition and behavior emerge from brain network interactions, suggesting that causal
interactions should be central to the study of brain function. Yet, approaches that …

This article investigates whether high-growth firms grow in different ways from other firms.
Specifically, we analyze how firms grow along several dimensions (growth of sales …

Machine learning algorithms are designed to capture complex relationships between
features. In this context, the high dimensionality of data often results in poor model …