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Robust Counterfactual Impact Evaluation/Evidence
Method evidence record

Robust Counterfactual Impact Evaluation

Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Robust Counterfactual Impact Evaluation
Taxonomic method record · regression-model / causal-inference
  • Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604. · URL
  • Ferrara, A. R., McCann, P., Pellegrini, G., Stelder, D., & Terribile, F. (2017). Assessing the impacts of Cohesion Policy on EU regions: A non-parametric analysis on interventions with multiple treatment intensities. Environment and Planning C: Politics and Space, 35(8), 1467–1487. · URL
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Related methods

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Taxonomic bucketCounterfactual Impact Evaluationmachine-suggested · Relational suggestion, not evidence.Same method familyDifference-in-Differencesmachine-suggested · Relational suggestion, not evidence.Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.See alsoPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSensitivity Analysis for Causalitymachine-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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