Pages that link to "Item:Q92189"

The following pages link to Doubly Robust Estimation in Missing Data and Causal Inference Models (Q92189):

Displaying 50 items.

• Doubly robust estimation and robust empirical likelihood in generalized linear models with missing responses (Q6190660) (← links)
• Testing the missing at random assumption in generalized linear models in the presence of instrumental variables (Q6196805) (← links)
• Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators (Q6199660) (← links)
• Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach (Q6204952) (← links)
• Causal survival analysis under competing risks using longitudinal modified treatment policies (Q6205052) (← links)
• Generalizing group fairness in machine learning via utilities (Q6535425) (← links)
• An averaging estimator for two-step m-estimation in semiparametric models (Q6536817) (← links)
• A simple multiply robust estimator for missing response problem (Q6537846) (← links)
• Doubly robust-type estimation of population moments and parameters in biased sampling (Q6541516) (← links)
• Robust nonparametric estimation of average treatment effects: a propensity score-based varying coefficient approach (Q6548901) (← links)
• Model misspecification and bias for inverse probability weighting estimators of average causal effects (Q6550300) (← links)
• A calibration method to stabilize estimation with missing data (Q6554765) (← links)
• Estimation of causal effects with a binary treatment variable: a unified M-estimation framework (Q6561146) (← links)
• A review of hot deck imputation for survey non-response (Q6574875) (← links)
• Minimax weight learning for absorbing MDPs (Q6581344) (← links)
• Optimal Covariate Balancing Conditions in Propensity Score Estimation (Q6586890) (← links)
• Identifying and estimating effects of sustained interventions under parallel trends assumptions (Q6589242) (← links)
• Improved inference for doubly robust estimators of heterogeneous treatment effects (Q6589258) (← links)
• Prior and posterior checking of implicit causal assumptions (Q6589259) (← links)
• A unified inference framework for multiple imputation using martingales (Q6593380) (← links)
• Robust propensity score weighting estimation under missing at random (Q6595778) (← links)
• The how and why of Bayesian nonparametric causal inference (Q6601995) (← links)
• Propensity score matching for estimating a marginal hazard ratio (Q6615932) (← links)
• Exposure effects on count outcomes with observational data, with application to incarcerated women (Q6616359) (← links)
• Efficient Augmented Inverse Probability Weighted Estimation in Missing Data Problems (Q6616599) (← links)
• A tutorial on dealing with time-varying eligibility for treatment: comparing the risk of major bleeding with direct-acting oral anticoagulant vs warfarin (Q6617399) (← links)
• A pilot design for observational studies: using abundant data thoughtfully (Q6617428) (← links)
• Formulating causal questions and principled statistical answers (Q6617441) (← links)
• Flexible template matching for observational study design (Q6617535) (← links)
• deepAFT: a nonlinear accelerated failure time model with artificial neural network (Q6618381) (← links)
• Targeted learning in observational studies with multi-valued treatments: an evaluation of antipsychotic drug treatment safety (Q6618441) (← links)
• Introduction to computational causal inference using reproducible Stata, R, and Python code: a tutorial (Q6622245) (← links)
• Targeted learning with daily EHR data (Q6624675) (← links)
• Doubly robust estimation of the weighted average treatment effect for a target population (Q6625556) (← links)
• Double Machine Learning for Sample Selection Models (Q6626262) (← links)
• Matching Using Sufficient Dimension Reduction for Causal Inference (Q6626364) (← links)
• Optimal sampling for design-based estimators of regression models (Q6626803) (← links)
• Causal inference methods for vaccine sieve analysis with effect modification (Q6626807) (← links)
• Covariate adjustment in randomized clinical trials with missing covariate and outcome data (Q6626916) (← links)
• Parametric and nonparametric propensity score estimation in multilevel observational studies (Q6626946) (← links)
• Estimation and evaluation of individualized treatment rules following multiple imputation (Q6626955) (← links)
• A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials (Q6627210) (← links)
• Safety surveillance and the estimation of risk in select populations: flexible methods to control for confounding while targeting marginal comparisons via standardization (Q6627310) (← links)
• Causal inference with noisy data: bias analysis and estimation approaches to simultaneously addressing missingness and misclassification in binary outcomes (Q6627315) (← links)
• Extending inferences from a randomized trial to a new target population (Q6627399) (← links)
• Robust estimation of the causal effect of time-varying neighborhood factors on health outcomes (Q6627464) (← links)
• A causal framework for classical statistical estimands in failure-time settings with competing events (Q6627538) (← links)
• Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: a multiply robust approach (Q6627647) (← links)
• Using propensity scores to estimate effects of treatment initiation decisions: state of the science (Q6627701) (← links)
• Propensity score weighting for covariate adjustment in randomized clinical trials (Q6627930) (← links)