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Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice.
Stable weights that balance covariates for estimation with incomplete outcome data
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Jersey Neyman · 1923
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Donald B Rubin · 1974
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The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Efficient estimation of average treatment effects using the estimated propensity score
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Bounded, efficient and doubly robust estimation with inverse weighting
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L1-penalized quantile regression in high-dimensional sparse models
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Statistics for high-dimensional data: methods, theory and applications
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Cross-fitting and fast remainder rates for semiparametric estimation
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Robust estimation of causal effects via high-dimensional covariate balancing propensity score
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Characterization of parameters with a mixed bias property
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