When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After t...

We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic active learning scheme that identifies the intervention which is optimally infor...

In this paper, we apply a recently developed differential approach to inference in staged tree models to causal inference. Staged trees generalise modelling techniques established for Bayesian networks (BN). They have the advantage that they can depict highly nuanced structure impossible to express in a BN and also enable us to perform causal manipulations associated with very general types of ...

Health information technology evaluators need to distinguish between intervention efficacy as assessed in the ideal circumstances of clinical trials and intervention effectiveness as assessed in the real world circumstances of actual practice. Because current evaluation study designs do not routinely allow for this distinction, we have developed a framework for evaluation of implementation fide...

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...

The methodology of causal analysis in the social sciences is often divided into two ideal type research scenarios: experimental social science and observational social science. For experimental social science, the researcher can manipulate the cause of interest. The most common research design is one where the analyst assigns values of the cause according to a randomization scheme and then calc...