This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in w...
Recent work in applying causal modeling (Bayesian networks) to software engineering has resulted in improved decision support systems for software project managers. Once the causa...
Peter Hearty, Norman E. Fenton, Martin Neil, Patri...
The importance of bringing causality into play when designing feature selection methods is more and more acknowledged in the machine learning community. This paper proposes a filt...
We consider an information-theoretic objective function for statistical modeling of time series that embodies a parametrized trade-off between the predictive power of a model and...
Susanne Still, James P. Crutchfield, Christopher J...
: This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional stati...