Monograph10.1017/CBO9780511803161
Causality: models, reasoning, and inference
14.8K
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.
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References
Nonparametric goodness-of-fit tests for the rasch model
TL;DR: In this article, a Monte Carlo algorithm realizing a family of nonparametric tests for the Rasch model is introduced which are conditional on the item and subject marginals, based on random changes of elements of data matrices without changing the marginals; most powerful tests against all alternative hypotheses are given for which a monotone characteristic may be computed from the data matrix.
•Proceedings Article
From data mining to knowledge discovery: an overview
Usama M. Fayyad,Gregory Piatetsky-Shapiro,Padhraic Smyth +2 more
- 01 Feb 1996
The Theory of the Design of Experiments
TL;DR: This well-organized book can serve as a cornerstone in a graduate student’s exploration in the theoretical aspects of experimental design and is a valuable reference for statisticians working in medicine, agriculture, the physical sciences, and other areas of biometry and industry.