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Heterogeneous Treatment Effect Inverse Probability Weighting/Evidence
Method evidence record

Heterogeneous Treatment Effect Inverse Probability Weighting

HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.

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Heterogeneous Treatment Effect Estimation via Inverse Probability Weighting
Taxonomic method record · regression-model / causal-inference
  • Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. · DOI 10.1111/1468-0262.00442
  • Abrevaya, J., Hsu, Y.-C., & Lieli, R. P. (2015). Estimating conditional average treatment effects. Journal of Business and Economic Statistics, 33(4), 485-505. · DOI 10.1080/07350015.2014.975555
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Related methods

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Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHeterogeneous Treatment Effect Propensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Same method familyInverse Probability Weightingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMarginal Structural Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPropensity Score Weightingmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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