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| Penyeimbangan Entropi× | Estimasi Robust Ganda (AIPW)× | |
|---|---|---|
| Bidang | Inferensi Kausal | Inferensi Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2012 | 2005 |
| Pencetus≠ | Jens Hainmueller | Robins & Rotnitzky; Bang & Robins |
| Tipe≠ | Covariate-balancing reweighting | Semiparametric causal estimator |
| Sumber perintis≠ | Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. DOI ↗ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| Alias | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Terkait≠ | 6 | 5 |
| Ringkasan≠ | Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
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