The present text comments on a paper by Daniel Steel, in which the author claims to extent from the deterministic to the general case the result according to which the causal Markov condition is satis ed by systems with jointly independent exogenous variables. I show that Steel's claim cannot be accepted unless one is prepared to abandon standard causal modeling terminology. Correlatively, I ar...

Understanding the precise nature of price sensitivities at the individual policyholder level is extremely valuable for insurers. A rate increase has a direct impact on the premium customers are paying, but there is also the indirect impact as a result of the ‘‘causal’’ effect of the rate change on the customer’s decision to renew the policy term. A rate increase may impair its intended impact o...

The purpose of this study was to test a model in which technology integration of pre-service teachers was predicted by a number of university-based and school-based factors. Initially, factors affecting technology integration were identified, and a research-based path model was developed to explain causal relationships between these factors. The results supported the hypothesized causal model. ...

People often make decisions with the goal of gaining information which can help reduce their uncertainty. However, recent work has suggested that people sometimes do not select the most diagnostic information queries available to them. A critical aspect of information search decisions is evaluating how obtaining a piece of information will alter a learner’s beliefs (e.g., a piece of information...

This paper relates our experience in developing a mechanism for reasoning about the di erential diagnosis of cases involving the symptoms of heart failure using a causal model of the cardiovascular hemodynamics with probabilities relating cause to e ect. Since the problem requires the determination of causal mechanism as well as primary cause, the model has many intermediate nodes as well as ca...

Random Differential Equations provide a natural extension of Ordinary Differential Equations to the stochastic setting. We show how, and under which conditions, every equilibrium state of a Random Differential Equation (RDE) can be described by a Structural Causal Model (SCM), while pertaining the causal semantics. This provides an SCM that captures the stochastic and causal behavior of the RDE...

We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernelbased conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y . Based on this assumption, we collect “votes” for hypothetical ...

Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, de­ termination of liability, and policy analysis. We present a method for evaluating counter­ factuals when the underlying causal model is represented by structural models a nonlin­ ear generalization of the simultaneous equa­ tions models commonly used in econometr...

Recovery of causal relationships in data is an essential part of scholarly inquiry in the social sciences. This chapter discusses strategies that have been successfully used in urban and regional economics for recovering such causal relationships. Essential to any successful empirical inquiry is careful consideration of the sources of variation in the data that identify parameters of interest. ...