• Korean Journal of Child Studies
• /
• v.34 no.6
• /
• pp.1-11
• /
• 2013

The purpose of this study was to investigate the process of counterfactual reasoning which children undergo, based on mental model theory and dual process theory. The subjects were 120 four-year-olds and 120 five-year-olds from Ulsan. Counterfactual reasoning task conditions were created, including task type and content, which were type 1-specific, type 1-general, type 2-specific, type 2-general. There were two stories used for each task condition. Children's counterfactual reasoning score range was 0 to 8. Data were analyzed using SPSS by mean, standard deviation, one sample t-test, repeated measures of Anova. The results of this study were as follows. First, children's counterfactual reasoning was above chance level regardless of the task condition. Second, children's counterfactual reasoning was lowest when type 1-specific or type 2-specific tasks were given, slightly higher when type1-general tasks were given, and the highest when type 2-general tasks were given. There was no significant difference between 4-year-old and 5-year-old children's counterfactual reasoning.

• Analyses & Alternatives
• /
• v.8 no.3
• /
• pp.125-150
• /
• 2024

This paper explores the integration of artificial intelligence and causal inference in social science research, focusing on causal deep learning. We examine key theories including Pearl's Structural Causal Model, Rubin's Potential Outcomes Framework, and Schölkopf's Causal Representation Learning. Methodologies such as structural causal models with deep learning, counterfactual reasoning, and causal discovery algorithms are discussed. The paper presents applications in social media analysis, economic policy, public health, and education, demonstrating how causal deep learning enables nuanced understanding of complex social phenomena. Key challenges addressed include model complexity, causal identification, interpretability, and ethical considerations like fairness and privacy. Future research directions include developing new AI architectures, real-time causal inference, and multi-domain generalization. While limitations exist, causal deep learning shows significant potential for enhancing social science research and informing evidence-based policy-making, contributing to addressing complex social challenges globally.