In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993, 1995a, b), and we give conditions for non-confounding, homogeneity and collapsibility for causal e€ects without knowledge of a completely constructed causal diagram. We ®rst introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneit...

New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic inference and causal inference for models of reasonable size. Many domains, such as education, can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial and poten...

People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems. Here we present a Hierarchical Bayesian model based on compositionality and causality that can learn a wide range of natural (although simple) visual concepts, generalizing in human-like ways from just one image. We evaluated...

The development of high quality large-scale software systems within schedule and budget constraints is a formidable software engineering challenge. The modification of these systems to incorporate new and changing capabilities poses an even greater challenge. This modification activity must be performed without adversely affecting the quality of the existing system. Unfortunately, this objectiv...

We discuss the causal set approach to discrete quantum gravity. We begin by describing a classical sequential growth process in which the universe grows one element at a time in discrete steps. At each step the process has the form of a causal set and the “completed” universe is given by a path through a discretely growing chain of causal sets. We then introduce a method for quantizing this cla...

Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the observed behavior properly and/or succinctly. Moreover, discovered process models tend to have deadlocks and l...

Recent discussions in the philosophy of biology have brought into question some fundamental assumptions regarding evolutionary processes, natural selection in particular. Some authors argue that natural selection is nothing but a population-level, statistical consequence of lower-level events (Matthen and Ariew [2002]; Walsh et al. [2002]). On this view, natural selection itself does not involv...

There is a long tradition in both philosophy and psychology to separate process accounts from dependency accounts of causation. In this paper, we motivate a unifying account that explains people’s causal attributions in terms of counterfactuals defined over probabilistic generative models. In our experiments, participants see two billiard balls colliding and indicate to what extent ball A cause...