| Description |
1 online resource (1 volume) : illustrations |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
Can drinking coffee help people live longer? What makes a stock's price go up? Why did you get the flu? Causal questions like these arise on a regular basis, but most people likely have not thought deeply about how to answer them. This book helps you think about causality in a structured way: What is a cause, what are causes good for, and what is compelling evidence of causality? Author Samantha Kleinberg shows you how to develop a set of tools for thinking more critically about causes. You'll learn how to question claims, identify causes, make decisions based on causal information, and verify causes through further tests. Whether it's figuring out what data you need, or understanding that the way you collect and prepare data affects the conclusions you can draw from it, Why will help you sharpen your causal inference skills. -- Provided by publisher. |
| Contents |
Copyright; Table of Contents; Preface; Chapter 1. Beginnings; What is a cause?; How can we find causes?; Why do we need causes?; What next?; Chapter 2. Psychology; Finding and using causes; Perception; Inference and reasoning; Blame; Culture; Human limits; Chapter 3. Correlation; What is a correlation?; No correlation without variation; Measuring and interpreting correlation; What can we do with correlations?; Why isn't correlation causation?; Multiple testing and p-values; Causation without correlation; Chapter 4. Time; Perceiving causality; The direction of time. |
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When things change over timeUsing causes: It's about time; Time can be misleading; Chapter 5. Observation; Regularities; Mill's methods; Complex causes; Probabilities; Why probability?; From probabilities to causes; Simpson's paradox; Counterfactuals; The limits of observation; Chapter 6. Computation; Assumptions; No hidden common causes; Representative distribution; The right variables; Graphical models; What makes a graphical model causal?; From data to graphs; Measuring causality; Probabilistic causal significance; Granger causality; Now what?; Chapter 7. Experimentation. |
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Getting causes from interventionsRandomized controlled trials; Why randomize?; How to control; Who do results apply to?; When n=you; Reproducibility; Mechanisms; Are experiments enough to find causes?; Chapter 8. Explanation; Finding causes of a single event; When multiple causes occur; Explanations can be subjective; When did the cause happen?; Explanation with uncertainty; Separating type and token; Automating explanation; Causality in the law; But-for causes; Proximate causes; Juries; Chapter 9. Action; Evaluating causal claims; Strength; Consistency (repeatability); Specificity. |
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TemporalityBiological gradient; Plausibility and Coherence; Experiment; Analogy; From causes to policies; Context; Efficacy and effectiveness; Unintended consequences; Chapter 10. Onward; The need for causality; Key principles; Causation and correlation are not synonymous; Think critically about bias; Time matters; All experimentation is not better than all observation; A well-stocked toolbox; The need for human knowledge; Appendix A. Notes; Chapter 1. Beginnings; Chapter 2. Psychology; Chapter 3. Correlation; Chapter 4. Time; Chapter 5. Observation; Chapter 6. Computation. |
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Chapter 7. ExperimentationChapter 8. Explanation; Chapter 9. Action; Chapter 10. Onward; Bibliography; Index; About the Author. |
| Subject |
Causation.
|
|
Causation |
| Other Form: |
Print version: Kleinberg, Samantha. Why : A Guide to Finding and Using Causes. : O'Reilly Media, ©2015 9781491949641 |
| ISBN |
1491949643 |
|
9781491949641 |
|
9781491952191 |
|
1491952199 |
|
9781491949627 |
|
1491949627 |
|
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