Regression modelQuasi-experimental / causal inference

Heterogeneous Treatment Effect Counterfactual Impact Evaluation

Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps the Conditional Average Treatment Effect (CATE) across the covariate space, revealing who benefits most or least from an intervention.

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Sources

  1. Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421-449. DOI: 10.1111/j.1475-4932.2009.00615.x
  2. Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational Studies, 5(2), 37-51. link

Related methods

ScholarGateHeterogeneous treatment effect Counterfactual impact evaluation (Heterogeneous Treatment Effect Counterfactual Impact Evaluation). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-counterfactual-impact-evaluation