The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms

Causal search algorithms have been effectively applied in different fields including biology, genetics, climate science, medicine, and neuroscience. However, there have been scant applications of these methods in social and behavioral sciences. This article provides an illustrative example of how causal search algorithms can shed light on important social and behavioral problems by using these algorithms to find the proximal mechanisms of academic achievement. Using a nationally representative data set with a wide range of relevant contextual and psychological factors, I implement four causal search procedures that varied important dimensions in the algorithms. Consistent with previous research, the algorithms identified prior achievement, executive functions (in particular, working memory, cognitive flexibility, and attentional focusing), and motivation as direct causes of academic achievement. I discuss the advantages and limitations of graphical models in general and causal search algorithms in particular for understanding social and behavioral problems.

A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.

Direction-dependence analysis

The concept of direction dependence has attracted growing attention due to its potential to help decide which of two competing linear regression models ( X → Y or Y → X) is more likely to reflect the correct causal flow. Several tests have been proposed to evaluate hypotheses compatible with direction dependence. In this issue, Thoemmes (2015) reports results of an empirical evaluation of direction-dependence tests using real-world data sets with known causal ordering and concludes that the tests (known to perform excellent in simulation studies) perform poorly in the real-world setting. The present article aims at answering the question how this is possible. First, we review potential conceptual issues associated with Thoemmes’ (2015) approach. We argue that direction dependence is best conceptualized as a confirmatory approach to test focused directional theories. Thoemmes’ (2015) evaluation is based on an exploratory use of direction dependence. It implicitly follows the tradition of causal search algorithms. Second, we discuss potential statistical issues associated with Thoemmes’ (2015) selection schemes used to decide whether a variable pair is suitable for direction-dependence analysis. Based on these issues, new tests of direction dependence as well as new guidelines for confirmatory direction-dependence analysis are proposed. An empirical example is given to illustrate the application of these guidelines.

Lay Theories of Emotion: 4. Reactions to Negative and Positive Emotional Episodes

Seven studies assessed the relation between lay theories of emotion (“threat” and “benefit” appraisal) and cognitions and behaviors in positive and negative emotional episodes. Studies 1 and 2 examined such a relation via the assessment of the habitual cognitions and behaviors persons evince in negative (Study 1) and positive emotional states. Studies 3 through 7 assessed whether and how appraisals of emotion affect some frequently observed cognitive-behavioral consequences of positive and negative affect induction, such as self-focused off-task cognitions, causal attribution, helping behavior, optimism, and creativity. Threat appraisal of emotion was related to negative self-evaluation, off-task cognitions, pessimism, and passivity during negative emotions, and to causal search during positive emotions. Benefit appraisal was related to active coping with, and emotional expressiveness of negative emotions and to the generalization of positive emotions to other behavioral-cognitive areas (altruism, optimism, creativity). The results are discussed in terms of a goal approach to emotion and personality.