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Causal inference

Definition
AI-generated

Purposefully joining design, assumptions, and estimands to support cause–effect claims—Mendelian randomization operationalizes genetics pieces.

Topics
Regression and multivariate statistics
Related
Biological Causality Causality Statistical Causality

Why it matters in GWAS

Statistical concepts underpin GWAS significance, effect estimation, relatedness random effects, multiple testing, fine-mapping priors, and post-GWAS multivariate methods.

Example usage

"The causal inference framework was used to separate likely causal effects from confounding and reverse causation."

Biological Causality Causality Statistical Causality Genome-Wide Association Study (GWAS) Linear Mixed Model Generalized Linear Mixed Model Statistical Power Effect Size Bonferroni Correction False Discovery Rate Mendelian Randomization Collider Bias
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Casella G, Berger RL. (2002). Statistical Inference. Duxbury Press.
  • Wasserman L. (2004). All of Statistics. Springer.
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