nowadays we know effective supply chain management a key to business success. therefore, supply chain managers use many practices for scm effectiveness and many tools as their enablers. nevertheless, literature about causal relations between sc enablers and scm practices and performance is scarce. this study reports a cognitive mapping of causal relationships between scm practices, sc enablers ...

Understanding causes and effects is important in many parts of life, especially when decisions have to be made. The systematic inference of causal models remains a challenge though. In this thesis, we study (1) “approximative” and “integrative” inference of causal models and (2) causal models as a basis for decision making in complex systems. By “integrative” here we mean including and combinin...

Reasoning with conditionals involving causal content is known to be affected by retrieval of alternative and disabling conditions. Recent evidence indicates that besides the number of stored conditions, the relative strength of association of the alternative conditions with the consequent term is another important factor that affects the retrieval process. In this study we examined the effect o...

This paper describes the theoretical underpinnings and architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutoring systems. The environment is based on a progression of increasingly sophisticated causal models that simulate domain phenomena, generate explanations, and serve as student models. Constraints on model evolution are discuss...

The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We...

Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X . It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential I...

STATISTICAL ANALYSIS IN EMPIRICAL BAYES AND IN CAUSAL INFERENCE

of the Dissertation Complete Identification Methods for Causal Inference

The paper addresses uncertain reasoning on a causal model given by two layered networks, where nodes in one layer express possible causes and those in the other are possible e$ects. Uncertainty of causalities is expressed in a novel manner, i.e. by Conditional Causal Possibilities. The expression has two advantages over the conventional way with conditional possibilities: it expresses the exact...

The central aims of this experiment were to compare observational and interventional learning of a simple causal chain, and to ascertain whether people represent their interventions in accordance with the normative model proposed by Pearl (2000). In the observation condition people treated putative causes as independent, and systematically selected the wrong model. In the intervention condition...