Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …

We propose an adversarial training procedure for learning a causal implicit generative
model for a given causal graph. We show that adversarial training can be used to learn a …

Perhaps no other topic in risk analysis is more difficult, more controversial, or more important
to risk management policy analysts and decision-makers than how to draw valid, correctly …

Complex dynamical systems are ubiquitous in many areas, including geoscience,
engineering, neural science, material science, etc. Modeling and predicting complex …

Granger causality is a fundamental technique for causal inference in time series data,
commonly used in the social and biological sciences. Typical operationalizations of Granger …

We study the problem of causal structure learning when the experimenter is limited to
perform at most $ k $ non-adaptive experiments of size $1 $. We formulate the problem of …

We consider the problem of learning a causal graph over a set of variables with
interventions. We study the cost-optimal causal graph learning problem: For a given …

Cortical processing of task-relevant information enables recognition of behaviorally
meaningful sensory events. It is unclear how task-related information is represented within …

Graph signal processing deals with signals which are observed on an irregular graph
domain. While many approaches have been developed in classical graph theory to cluster …