Model Averaging Methods for Weight Trimming

By Michael R. Elliott. 2008

48 citations recorded by the source corpus.

In sample surveys where sampled units have unequal probabilities of inclusion, associations between the inclusion probabilities and the statistic of interest can induce bias. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have highly variable weights, which can introduce undesirable variability in statistics such as the population mean or linear regression estimates. Weight trimming reduces large weights to a fixed maximum value, reducing variability but introducing bias. Most standard approaches are ad-hoc in that they do not use the data to optimize bias-variance tradeoffs. This manuscript develops variable selection models, termed “weight pooling” models, that extend weight trimming procedures in a Bayesian model averaging framework to produce “data driven” weight trimming estimators. We develop robust yet efficient models that approximate fully-weighted estimators when bias correction is of greatest importance, and approximate unweighted estimators when variance reduction is critical.

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Citations in the corpus

Balanced Policy Evaluation and Learning (2017, neutral): “Another stopgap measure is to clip the propensities [14, 20] resulting in the clipped IPW (CIPW) estimator: ⌧̂M -CIPW(⇡) = ⌧̂WM -CIPW(⇡), W M -CIPW i (⇡) = ⇡Ti(Xi)/max{M, '̂Ti(Xi)}.”
Determinants of adoption and disadoption of minimum tillage by cotton farmers in eastern Zambia (2016, methodology): “Using weights in the analysis reduces bias when generalizing to the broader population (cotton farmers in communities with relatively high adoption) and cannot be ignored when observations are stratified by the dependent variable (Elliott 2008) as was done here.”
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, background): “KEY WORDS: Causal inference; Observational study; Propensity score; Quadratic programming; Unit nonresponse; Weight adjustment 1.”
Inference for Nonprobability Samples (2017, background): “Simulation studies in Elliott and Little (2000) showed that exchangeable priors of the form (17) were somewhat fragile, tending to oversmooth when σ 2 and τ 2 were approximately equal.”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, background): “Since these weights can be extreme, both normalization [2, 31, 41] and clipping [10, 12, 40] are often employed.”