Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

By José R. Zubizarreta. 2015

437 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Covariate balancing propensity score by tailored loss functions (2016, methodology): “Zubizarreta (2015) proposed to solve a problem similar to (22) (the objective is replaced by the coefficient of variation of w and the exact balancing constraints are relaxed to tolerance level).”
Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing (2016, background): “ourse equivalent to ours, the only dierence being that Zubizarreta bounds imbalance in constraint form whereas we do so in Lagrange form. The main conceptual dierence between our setting and that ofZubizarreta (2015) is that he considers problem settings where p<n c, and then considers tto be a p”
The Impact of Machine Learning on Economics (2018, methodology): “Athey et al. (2016c) propose using a method they refer to as “residual balancing,” building on work on balancing weights by Zubizarreta (2015).”
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score. (2018, methodology): “…covariate balance between the treatment and control groups (e.g., Hainmueller, 2012; Graham et al., 2012; Imai & Ratkovic, 2014; Chan et al., 2016; Zubizarreta, 2015; Zhao, 2019; Fan et al., 2016).80 The proposed methodology extends the covariate balancing propensity score of Imai & Ratkovic…”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, methodology): “Z) among the units with observed data, i.e. it satises the "balancing" property P n[˚(Z)] ˇP n 2 4 D expit hb b;˚(Z)i ˚(Z) 3 5: 10 Estimators satisfying this property have been used in Zubizarreta (2015); Athey et al. (2018); Chan et al. (2016); Tan (2017); Imai and Ratkovic (2014); Hirsh”
Generalized Optimal Matching Methods for Causal Inference (2016, background): “This trade o↵ between bias and variance is well understood in matching [24, 25, 26, 13].”
The Balancing Act in Causal Inference (2021, methodology): “…existing approaches can be written as examples of Equation 12 with various choices of ζ and χ, including the stable balancing weights approach (Zubizarreta, 2015); the lasso minimum distance estimator of the inverse propensity score (Chernozhukov, Newey, et al., 2018); the regularized…”
Towards Out-Of-Distribution Generalization: A Survey (2021, methodology): “To solve such difﬁcult problem, motivated by the literature of variable balancing strategies [147], [148], [149], Shen et al. [118] propose to consider all the variables as the treat-ment and learn a set of global sample weights that could remove the confounding bias for all the potential…”
Synthetic Difference In Differences (2018, neutral): “ness, where pre-treatment covariates (usually called X) are taken to be noiseless [Athey, Imbens, and Wager, 2018, Graham, de Xavier Pinto, and Egel, 2012, Hainmueller, 2012, Imai and Ratkovic, 2014, Zubizarreta, 2015]. Here, however, the weights ^!and ^ are optimized to balance Y , not L, and have ”
Stable Prediction across Unknown Environments (2018, methodology): “To precisely estimate causal effects in the presence of many confounders, covariate balancing methods have been proposed [10], [13], [15], [16], [17], [18], [21].”
Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements (2018, methodology): “This contrasts with some other methods such as entropy balancing and stable weights that solve a convex optimization problem [Hainmueller (2012), Zubizarreta (2015)].”
Learning L2 Continuous Regression Functionals via Regularized Riesz Representers (2018, background): “3) are similar to those of Zubizarreta (2015) and Athey, Imbens, and Wager (2017) though the source of these constraints is l1 regularized best L2 approximation of the RR α(x) = π0(z) d − [1 − π0(z)](1 − d) by a linear combination of the dictionary b(x).”
Entropy Balancing is Doubly Robust (2015, background): “Recently, there is an emerging interest, particularly among applied researchers, in directly incorporating covariate balance in the estimation procedure, so there is no need to check covariate balance repeatedly (e.g. Diamond and Sekhon, 2013; Imai and Ratkovic, 2014; Zubizarreta, 2015).”