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Related Concept Videos

Theory of Attribution I: Correspondent Inference Theory01:15

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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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Theory of Attribution II: Kelley's Covariation Theory01:29

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Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods.

Wei Zhang1, Thomas Kobber Panum2, Somesh Jha1,3

  • 1Computer Scineces Department, University of Wisconsin-Madison, Madison, WI, USA.

Proceedings of Machine Learning Research
|December 4, 2020
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This summary is machine-generated.

We introduce CAUSE, a new framework to learn Granger causality in event sequences. CAUSE accurately identifies causal relationships between event types, outperforming existing methods.

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Area of Science:

  • Causal Inference
  • Machine Learning
  • Time Series Analysis

Background:

  • Learning Granger causality from complex event data is challenging.
  • Existing methods lack flexibility and explainability for diverse event interdependencies.

Purpose of the Study:

  • To develop a novel framework for inferring Granger causality between event types.
  • To address limitations in model flexibility and explainability of current approaches.

Main Methods:

  • Propose CAUSE (Causality from AttribUtions on Sequence of Events).
  • Implicitly capture event interdependencies using a neural point process.
  • Extract Granger causality statistics via axiomatic attribution.

Main Results:

  • CAUSE demonstrates superior performance in inferring inter-type Granger causality.
  • The framework effectively handles diverse event interdependencies across multiple datasets.
  • Outperforms a range of state-of-the-art methods.

Conclusions:

  • CAUSE provides a flexible and explainable approach to Granger causality learning.
  • The method is effective for asynchronous, interdependent, multi-type event sequences.
  • Advances the understanding of causal relationships in complex event data.