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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Causal Structural Learning on MPHIA Individual Dataset.

Le Bao1, Changcheng Li2, Runze Li1

  • 1Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.

Journal of the American Statistical Association
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

A new causal learning algorithm improves understanding of HIV epidemic drivers. It identifies key factors like age, condom use, and travel time to care, aiding progress toward ending the HIV epidemic.

Keywords:
90-90-90 targetsCausal structural learningHIVPHIA

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • The Population-based HIV Impact Assessment (PHIA) surveys track progress towards UNAIDS 90-90-90 targets.
  • Understanding drivers of HIV epidemics in sub-Saharan Africa is crucial for effective interventions.

Purpose of the Study:

  • To propose a novel causal structural learning algorithm for discovering key factors and pathways related to HIV epidemic control.
  • To improve upon existing algorithms by preserving more information about important features and causal relationships.

Main Methods:

  • Development of a novel causal structural learning algorithm designed to be less aggressive in edge removal than existing methods.
  • Application and validation of the algorithm using the Malawi PHIA (MPHIA) dataset.
  • Comparison and validation using Bayesian Information Criterion (BIC) and Monte Carlo simulations.

Main Results:

  • The algorithm identified significant factors influencing HIV awareness and treatment, including age, condom usage, number of sexual partners, and travel time to HIV care facilities.
  • Results showed improved true positive rates in important feature discovery compared to existing algorithms.
  • Specific findings include age and condom use for female HIV awareness, sexual partners for male awareness, and travel time for treatment access.

Conclusions:

  • The proposed causal learning algorithm effectively identifies key drivers of HIV epidemics.
  • The algorithm offers an improvement over existing methods in discovering important features and causal pathways.
  • Findings provide valuable insights for public health strategies aimed at ending the HIV epidemic.