The widespread applicability of analytics in cyberphysical systems has motivated research into causal inference methods. Predictive estimators are not sufficient when analytics are used for decision making; rather, the flow of causal effects must be determined. Generally speaking, these methods focus on estimation of a causal structure from experimental data. In this paper, we consider the dual...

In this paper we give a review of recent causal inference methods. First, we discuss methods for causal structure learning from observational data when confounders are not present and have a close look at methods for exact identifiability. We then turn to methods which allow for a mix of observational and interventional data, where we also touch on active learning strategies. We also discuss me...

In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial regression discontinuity design articles. We also find no (i) published in the top five journals different; (ii) “revise resubmit” process mitigates problem; (iii) things improving through time. Kranz Pütz...

In this paper, we unify causal and non-causal feature selection methods based on the Bayesian network framework. We first show that the objectives of causal and non-causal feature selection methods are equal and are to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We demonstrate that causal and non-causal feature selection take different...