Regression modelQuasi-experimental / causal inference

Heterogeneous Treatment Effect Inverse Probability Weighting (HTE-IPW)

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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Sources

  1. 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
  2. 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

Related methods

ScholarGateHeterogeneous Treatment Effect Inverse Probability Weighting (Heterogeneous Treatment Effect Estimation via Inverse Probability Weighting). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-inverse-probability-weighting