Zapis dokaza metode
Heterogeneous Treatment Effects
Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).
Izvorni zapis
Citati kopirani doslovno iz izvornog zapisa metode. Ne impliciraju nikakvu provjeru na razini tvrdnje.
Heterogeneous Treatment Effects (CATE / Meta-Learners)
Taksonomski zapis metode · regression-model / causal-inference
- Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. · DOI 10.1080/01621459.2017.1319839
- Künzel, S. R., Sekhon, J. S., Bickel, P. J. & Yu, B. (2019). Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning. Proceedings of the National Academy of Sciences (PNAS). · DOI 10.1073/pnas.1804597116
Uređene tvrdnje
Tvrdnje pohranjene u knjigu dokaza, svaka s vlastitom procjenom.
Nema uređenih tvrdnji
Ovaj prikaz ne izmišlja procjenu tvrdnje kada knjiga dokaza nema nijednu.
Povezane metode
Generirano iz grafa metode i prikazano kao strojno predložene relacije — ne implicira se nikakva tvrdnja dokaza.