Linked from

The 13 pages that link to Instrumental variable, each with the reason it gives.

• Causal inferenceRelated: It can isolate treatment variation when unmeasured confounding prevents ordinary comparisons.

• ConfoundingRelated: An instrument can isolate treatment variation that is not linked to confounders.

• Observational studyRelated: Instrumental-variable analysis can address some unmeasured confounding under demanding assumptions.

• Natural experimentRelated: Some natural experiments use an external event as an instrument for exposure.

• EconometricsBroader topic: Instrumental variables can address confounding when randomized evidence is unavailable.

• Quasi-experimentRelated: It can address treatment selection when its assumptions are defensible.

• Econometric modelRelated: Instruments can identify relationships when regressors are correlated with the error term.

• Cowles CommissionRelated: The method addresses endogeneity central to estimating simultaneous economic equations.

• Random assignment (experimental design)Compared with: It offers a different route to causal estimates when random assignment is absent.

• Trygve HaavelmoRelated: This method addresses the endogeneity problems that arise in structural economic equations.

• Lars Peter HansenRelated: Instrumental-variable estimators can be expressed as special cases of GMM.

• Correlation does not imply causationRelated: Instrumental-variable analysis can address confounding when randomization is unavailable.

• James HeckmanCompared with: Instrumental-variable estimates can answer different questions from Heckman’s selection-based approach.