Qualitative models support interactive simulations that are well suited to help learners in acquiring causal interpretations of physical systems and their behavior. Such simulation models can be large, particularly if they include many subsystems. When simulations are too big they hardly can be used effectively for teaching purposes. They have to be reorganized into smaller sets of simulation m...

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: ...

Developing a theoretically driven causal model of state failure is necessary if scholars are committed to improving the predictive power and forecasting ability of early warning models of state failure. Building upon the work of the State Failure Project and earlier statistical models of state failure, this study develops a causal model of state failure that is based upon a theoretical foundati...

Causal knowledge is frequently pursued by researchers in many fields, such as medicine, economics, and social science, yet very little research in knowledge discovery focuses on discovering causal knowledge. Those researchers rely on a set of methods, called experimental and quasiexperimental designs, that exploit the ontological structure of the world to limit the set of possible statistical m...

A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about causal graphs from observational data that is reliable in the large sample limit, even when some of t...