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Causal Models (SEP)
Causal models are mathematical models representing causal relationships within an individual system or population. They facilitate inferences about causal relationships from statistical data. They can teach us a good deal about the epistemology of causation, and about the relationship between causation and probability. They have also been applied to topics of interest to philosophers, such as the logic of counterfactuals, decision theory, and the analysis of actual causation.
Causal modeling is an interdisciplinary field that has its origin in the statistical revolution of the 1920s, especially in the work of the American biologist and statistician Sewall Wright (1921). Important contributions have come from computer science, econometrics, epidemiology, philosophy, statistics, and other disciplines. Given the importance of causation to many areas of philosophy, there has been growing philosophical interest in the use of mathematical causal models. Two major works—Spirtes, Glymour, and Scheines 2000 (abbreviated SGS), and Pearl 2009—have been particularly influential.
A causal model makes predictions about the behavior of a system. In particular, a causal model entails the truth value, or the probability, of counterfactual claims about the system; it predicts the effects of interventions; and it entails the probabilistic dependence or independence of variables included in the model. Causal models also facilitate the inverse of these inferences: if we have observed probabilistic correlations among variables, or the outcomes of experimental interventions, we can determine which causal models are consistent with these observations. The discussion will focus on what it is possible to do in “in principle”. For example, we will consider the extent to which we can infer the correct causal structure of a system, given perfect information about the probability distribution over the variables in the system. This ignores the very real problem of inferring the true probabilities from finite sample data. In addition, the entry will discuss the application of causal models to the logic of counterfactuals, the analysis of causation, and decision theory.
Read the rest here: https://plato.stanford.edu/entries/causal-models/






















