Why

Table of contents

1. Praise for Samantha Kleinberg’s Why
2. Why
3. Why: A Guide to Finding and Using Causes
4. Preface
5. 1. Beginnings
   1. Where do our concepts of causality and methods for finding it come from?
   2. What is a cause?
   3. How can we find causes?
   4. Why do we need causes?
   5. What next?
6. 2. Psychology
   1. How do people learn about causes?
   2. Finding and using causes
      1. Perception
      2. Inference and reasoning
   3. Blame
   4. Culture
   5. Human limits
7. 3. Correlation
   1. Why are so many causal statements wrong?
   2. What is a correlation?
      1. No correlation without variation
      2. Measuring and interpreting correlation
   3. What can we do with correlations?
   4. Why isn’t correlation causation?
   5. Multiple testing and p-values
   6. Causation without correlation
8. 4. Time
   1. How does time affect our ability to perceive and reason with causality?
   2. Perceiving causality
   3. The direction of time
   4. When things change over time
   5. Using causes: It’s about time
   6. Time can be misleading
9. 5. Observation
   1. How can we learn about causes just by watching how things work?
   2. Regularities
      1. Mill’s methods
      2. Complex causes
   3. Probabilities
      1. Why probability?
      2. From probabilities to causes
   4. Simpson’s paradox
   5. Counterfactuals
   6. The limits of observation
10. 6. Computation
    1. How can the process of finding causes be automated?
    2. Assumptions
       1. No hidden common causes
       2. Representative distribution
       3. The right variables
    3. Graphical models
       1. What makes a graphical model causal?
       2. From data to graphs
    4. Measuring causality
       1. Probabilistic causal significance
       2. Granger causality
    5. Now what?
11. 7. Experimentation
    1. How can we find causes by intervening on people and systems?
    2. Getting causes from interventions
    3. Randomized controlled trials
       1. Why randomize?
       2. How to control
       3. Who do results apply to?
    4. When n=you
    5. Reproducibility
    6. Mechanisms
    7. Are experiments enough to find causes?
12. 8. Explanation
    1. What does it mean to say that this caused that?
    2. Finding causes of a single event
       1. When multiple causes occur
       2. Explanations can be subjective
       3. When did the cause happen?
    3. Explanation with uncertainty
    4. Separating type and token
    5. Automating explanation
    6. Causality in the law
       1. But-for causes
       2. Proximate causes
       3. Juries
13. 9. Action
    1. How do we get from causes to decisions?
    2. Evaluating causal claims
       1. Strength
       2. Consistency (repeatability)
       3. Specificity
       4. Temporality
       5. Biological gradient
       6. Plausibility and Coherence
       7. Experiment
       8. Analogy
    3. From causes to policies
       1. Context
       2. Efficacy and effectiveness
       3. Unintended consequences
14. 10. Onward
    1. Why causality now?
    2. The need for causality
    3. Key principles
       1. Causation and correlation are not synonymous
       2. Think critically about bias
       3. Time matters
       4. All experimentation is not better than all observation
    4. A well-stocked toolbox
    5. The need for human knowledge
15. Acknowledgments
16. A. Notes
    1. Chapter 1. Beginnings
    2. Chapter 2. Psychology
    3. Chapter 3. Correlation
    4. Chapter 4. Time
    5. Chapter 5. Observation
    6. Chapter 6. Computation
    7. Chapter 7. Experimentation
    8. Chapter 8. Explanation
    9. Chapter 9. Action
    10. Chapter 10. Onward
17. Bibliography
18. Index
19. About the Author
20. Colophon