Causality, mosaics, and the health sciences

Interpreting causality in the health sciences

We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms, or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences...

Causality in the Sciences

This paper presents a general theory of causation based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory subsumes and unifies current approaches to causation, including graphical, potential outcome, probabilistic, decision analytical, and structural equation models, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and cou...

Causality and Probability in the Sciences

Causality in the Social and Behavioral Sciences

This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illustrated using a general theory of causation based on nonparametric structural equation models (SEM)...

Jon Williamson Causality and probability in the sciences

Causality and probability in the sciences Towards the end of the nineteenth century Karl Pearson noted that a prob-abilistic dependence between two variables does not necessarily imply that the two variables are causally connected (Pearson, 1897). This led Pearson, in the third (1911) edition of his book Pearson (1892), to argue that talk of cause and effect should be eradicated from science in...