Skip to content

Statistical Causality

Definition
AI-generated

Statistical Causality denotes cause–effect conclusions supported by an explicit estimand, design, and identifying assumptions—often framed with potential outcomes, structural models, or directed acyclic graphs—rather than by correlation alone.

Topics
Probability and inference GWAS
Related
Causality Biological Causality Causal Inference

Why it matters in GWAS

Association p-values and effect sizes from population GWAS do not by themselves identify a causal effect of an exposure, gene product, or variant without additional assumptions; statistical causal reasoning clarifies what is (and is not) identified from summary statistics, relatedness structure, and possible colliders or pleiotropy.

Example usage

"We treated MR as a statistical causality exercise: the estimand was the effect of lifelong higher LDL, not the marginal SNP–trait association from the discovery GWAS."

Causality Biological Causality Causal Inference Collider Bias Genome-Wide Association Study (GWAS) Instrumental Variable Mendelian Randomization
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

  • Hernán MA, Robins JM. (2020). Causal inference: what if. Chapman & Hall/CRC.
  • Imbens GW, Rubin DB. (2015). Causal inference for statistics, social, and biomedical sciences. Cambridge University Press.
← Standard error of the mean Coefficient →

Last updated (UTC · Git history)