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34 results

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Time and Causality across the Sciences
  • Edited by Samantha Kleinberg
  • Published online:
    24 September 2019
    Print publication:
    26 September 2019
  • This book, geared toward academic researchers and graduate students, brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Research across disciplines shows that the relationship is much more complex than that. This book explores what that means for both the metaphysics and epistemology of causes - what they are and how we can find them. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models.

Causality, Probability, and Time
  • Samantha Kleinberg
  • Published online:
    05 December 2012
    Print publication:
    12 November 2012
  • Causality is a key part of many fields and facets of life, from finding the relationship between diet and disease to discovering the reason for a particular stock market crash. Despite centuries of work in philosophy and decades of computational research, automated inference and explanation remains an open problem. In particular, the timing and complexity of relationships has been largely ignored even though this information is critically important for prediction, explanation and intervention. However, given the growing availability of large observational datasets including those from electronic health records and social networks, it is a practical necessity. This book presents a new approach to inference (finding relationships from a set of data) and explanation (assessing why a particular event occurred), addressing both the timing and complexity of relationships. The practical use of the method developed is illustrated through theoretical and experimental case studies, demonstrating its feasibility and success.

  • 5 - Inferring Causality

  • Samantha Kleinberg, Stevens Institute of Technology, New Jersey
  • Book:
    Causality, Probability, and Time
    Published online:
    05 December 2012
    Print publication:
    12 November 2012, pp 111-141

  • Summary

    Thus far, I have discussed the types of causes that will be identified, how they can be represented as logical formulas, and how the definitions hold up to common counterexamples. This chapter addresses how these relationships can be inferred from a set of data. I begin by examining the set of hypotheses to be tested, the types of data one may make inferences from, and how to determine whether formulas are satisfied directly in this data (without first inferring a model). Next, I discuss how to calculate the causal significance measure introduced in the previous chapter (ϵavg) in data, and how to determine which values of this measure are statistically significant. I then address inference of relationships and their timing without prior knowledge of either. The chapter concludes by examining theoretical issues including the computational complexity of the testing procedures.

    Testing Prima Facie Causality

    Chapter 4 introduced a measure for causal significance and showed how probabilistic causal relationships can be represented using probabilistic temporal logic formulas. This representation allows efficient testing of arbitrarily complex relationships. In this chapter, I adapt standard PCTL model checking procedures to validate formulas directly in a set of time series data without first inferring a model (as this can be computationally complex or infeasible in many cases).

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