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Luke W. Miratrix – Grantee Submission, 2022
We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit and cannot obtain comparable controls. For example, with recent county- and state-wide criminal justice reform efforts, where judicial bodies have changed bail setting…
Descriptors: Causal Models, Case Studies, Quasiexperimental Design, Monte Carlo Methods
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Hitchcock, John H.; Johnson, R. Burke; Schoonenboom, Judith – Research in the Schools, 2018
The central purpose of this article is to provide an overview of the many ways in which special educators can generate and think about causal inference to inform policy and practice. Consideration of causality across different lenses can be carried out by engaging in multiple method and mixed methods ways of thinking about inference. This article…
Descriptors: Causal Models, Statistical Inference, Special Education, Educational Research
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Ulriksen, Marianne S.; Dadalauri, Nina – International Journal of Social Research Methodology, 2016
Single case studies can provide vital contributions to theory-testing in social science studies. Particularly, by applying the process-tracing method, case studies can test theoretical frameworks through a rigorous research design that ensures substantial empirical leverage. While most scholarly contributions on process-tracing focus on either…
Descriptors: Case Studies, Hypothesis Testing, Social Science Research, Research Methodology
Imbens, Guido W.; Rubin, Donald B. – Cambridge University Press, 2015
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding…
Descriptors: Causal Models, Statistical Inference, Statistics, Social Sciences
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Griffiths, Thomas L.; Tenenbaum, Joshua B. – Psychological Review, 2009
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…
Descriptors: Causal Models, Prior Learning, Logical Thinking, Statistical Inference