Posttraumatic Stress in Young Children: Causal Analysis of 1,354 At-Risk Cases

A causal network model of 251 variables and 818 bivariate relations reveals direct causes and effects of posttraumatic stress in young children. The analysis applies Causal Data Science methods to an existing longitudinal dataset from the Consortium for Longitudinal Studies in Child Abuse and Neglect (LONGSCAN). Glenn N. Saxe authored this research, last updated in April 2026.

Use Cases

• Causal modeling of posttraumatic stress based on identified risk factors like emotional maltreatment and witnessing violence.
• Longitudinal analysis of child development based on data collected from infancy to age 8.
• Validation of Causal Data Science algorithms applied to complex, real-world observational data.

Strengths

• Dataset includes 1,354 children identified as maltreated or at risk.
• Analysis produced a causal network model with 251 variables and 818 bivariate relations.
• Longitudinal data spans from infancy to age 8.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• The primary data file is a 33.4 KB DOCX document, which suggests limited raw data scope.
• Row count for the underlying dataset is unknown, which may limit suitability assessment.

Provenance

Source

Consortium for Longitudinal Studies in Child Abuse and Neglect (LONGSCAN)

Collection Method

Observational, longitudinal data collection.

Time Range

Data likely covers from infancy to age 8.

Freshness

Last updated 2026-04-22 05:49:28