Skip to content

A priori causal-graph specification before educational data analysis

Research Method · All research methods
Evidence · no claims cited

Description

The paper argues that researchers should articulate their causal assumptions graphically before collecting or analyzing data, because "there are no causal conclusions without causal assumptions". Explicit assumptions make it possible to trace whether a proposed causal effect is identified given the assumed structure, and to detect confounding, overcontrol, and collider bias before they distort estimates.

Accounts

  • Make causal assumptions explicit before data analysis to expose and prevent bias: The paper argues that researchers should articulate their causal assumptions graphically before collecting or analyzing data, because "there are no causal conclusions without causal assumptions". Explicit assumptions make it possible to trace whether a proposed causal effect is identified given the assumed structure, and to detect confounding, overcontrol, and collider bias before they distort estimates. (Weidlich et al. (2024))

Claims

-

Key Sources

  • Weidlich, J., Hicks, B., & Drachsler, H. (2024). Causal reasoning with causal graphs in educational technology research. Education Tech Research Dev. https://doi.org/10.1007/s11423-023-10241-0
Page metadata
Field Value
type research-method
id a-priori-causal-graph-specification-before-educational-data-analysis
title A priori causal-graph specification before educational data analysis
description The paper argues that researchers should articulate their causal assumptions graphically before collecting or analyzing data, because "there are no causal conclusions without causal assumptions".
status review
generated by process:settle-candidates on 2026-10-29
trust unverified — no one has confirmed this page's content

Frontmatter is the page's machine-readable record — see the Schema & Guide.