Causality: Models, Reasoning and Inference

This book offers a comprehensive exposition of modern analysis of causation. It shows how causality has grown into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Propensity Scores are discussed in detail from Page 348 in the unit titled Understanding Propensity Scores.

Contents

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 social science and economics;
6. Simpson's paradox, confounding, and collapsibility;
7. The logic of structure-based counterfactuals;
8. Imperfect experiments: bounding effects and counterfactuals;
9. Probability of causation: interpretation and identification;
10. The actual cause.

Source

Pearl J (2009) “Understanding propensity scores”. In Causality: Models, Reasoning, and Inference. (Cambridge: Cambridge University Press). Retrieved from http://www.cambridge.org/aus/catalogue/catalogue.asp?isbn=9780521895606

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Author
Research Assistant, RMIT University.
Melbourne.

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