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Heterogeneous treatment effect Causal impact analysis/Evidence
Method evidence record

Heterogeneous treatment effect Causal impact analysis

Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit most or least from an intervention.

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Source record

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Heterogeneous Treatment Effect Causal Impact Analysis
Taxonomic method record · regression-model / causal-inference
  • Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. · DOI 10.1214/14-AOAS788
  • Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. · DOI 10.1073/pnas.1510489113
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketCausal Impact Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHeterogeneous Treatment Effect Difference-in-Differencesmachine-suggested · Relational suggestion, not evidence.Same method familyInterrupted Time Seriesmachine-suggested · Relational suggestion, not evidence.See alsoPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSynthetic Control Methodmachine-suggested · Relational suggestion, not evidence.

Evidence status

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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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