Process / pipelinecausal inference method
Instrumental Variables (IV) Method for Causal Inference
Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.
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Sources
- Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗
- Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak. Journal of the American Statistical Association, 90(430), 443-450. DOI: 10.1080/01621459.1995.10476536 ↗
- Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). Cambridge, MA: MIT Press. link ↗
Related methods
Referenced by
Anderson-Hsiao IVArellano-Bond GMM estimatorBayesian Fuzzy Regression DiscontinuityBayesian Instrumental VariablesBayesian Regression Discontinuity DesignBayesian Sensitivity Analysis for CausalityCausal Discovery AlgorithmsCounterfactual Impact EvaluationCounterfactual Impact Evaluation in Education ResearchDAG Causal IdentificationDifference-in-DifferencesDifference-in-Differences in Education ResearchDifference-in-DiscontinuitiesDynamic Fuzzy Regression DiscontinuityDynamic Instrumental VariablesEvent Study Design in Education ResearchFixed Effects Panel ModelFourier Hausman testFuzzy Regression DiscontinuityFuzzy Regression Discontinuity in Education ResearchGMM EstimationHeterogeneous Treatment Effect Fuzzy Regression DiscontinuityHeterogeneous treatment effect Instrumental variablesInstrumental Variables in Education ResearchInverse Probability Weighting in Education ResearchMachine Learning-Augmented Fuzzy Regression DiscontinuityMachine learning-augmented instrumental variablesMachine Learning-Augmented Placebo TestMachine Learning-Augmented Sensitivity Analysis for CausalityMarginal structural model in education researchMulti-period Fuzzy Regression DiscontinuityNetwork EconometricsNonlinear difference GMMNonlinear Hausman testNonlinear System GMMOrdinary Least SquaresPanel Data Fuzzy Regression DiscontinuityPanel Data Instrumental VariablesPanel Fixed EffectsPlacebo Test in Education ResearchPolicy Evaluation Counterfactual Impact EvaluationPolicy Evaluation Fuzzy Regression DiscontinuityPolicy Evaluation Instrumental VariablesPolicy Evaluation Matching EstimatorPolicy Evaluation Panel Event StudyPolicy Evaluation Placebo TestPolicy Evaluation Regression Discontinuity DesignPolicy Evaluation Synthetic Control MethodProbit ModelPropensity Score Weighting in Education ResearchRandom Effects ModelRegression Discontinuity DesignRegression discontinuity design in education researchRegression Kink DesignRobust Fuzzy Regression DiscontinuityRobust Instrumental VariablesRobust Regression Discontinuity DesignRobust System GMMSensitivity Analysis for CausalitySensitivity analysis for causality in education researchSpatial Counterfactual Impact EvaluationSpatial Fuzzy Regression DiscontinuitySpatial Instrumental VariablesSpatial Regression Discontinuity DesignSpatial Sensitivity Analysis for CausalitySynthetic Control MethodThree-Stage Least Squares