Pub. date: 2011 | Online Pub. Date: October 04, 2011 | DOI: 10.4135/9781412994163 | Print ISBN: 9781412959636 | Online ISBN: 9781412994163| Publisher:SAGE Publications, Inc.About this encyclopedia
F. Daniel Hidalgo & Jasjeet S. Sekhon
Causality refers to the relationship between events where one set of events (the effects) is a direct consequence of another set of events (the causes). Causal inference is the process by which one can use data to make claims about causal relationships. Since inferring causal relationships is one of the central tasks of science, it is a topic that has been heavily debated in philosophy, statistics, and the scientific disciplines. This entry reviews the models of causation and tools for causal inference most prominent in the social sciences, including regularity approaches, associated with David Hume, and counterfactual models, associated with Jerzy Splawa-Neyman, Donald Rubin, and David Lewis, among many others. One of the most notable developments in the study of causation is the increasing unification of disparate methods around a common conceptual and mathematical language that treats causality in counterfactual terms—that is, the Neyman-Rubin model. This entry discusses how counterfactual ...