Doubly robust treatment effect estimation with missing attributes
DOI10.1214/20-AOAS1356zbMath1470.62160arXiv1910.10624OpenAlexW3088263234MaRDI QIDQ2044265
Erik Sverdrup, Jean-Denis Moyer, Julie Josse, Stefan Wager, Tobias Gauss, Imke Mayer
Publication date: 4 August 2021
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.10624
missing datacausal inferencepotential outcomesobservational datapublic healthincomplete confoundersmajor traumapropensity score estimation
Nonparametric robustness (62G35) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Missing data (62D10)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy
- Robust inference on average treatment effects with possibly more covariates than observations
- Generalized random forests
- Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
- Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework
- Consistency of random forests
- Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness
- Estimating treatment effects with partially observed covariates using outcome regression with missing indicators
- Dealing with limited overlap in estimation of average treatment effects
- The central role of the propensity score in observational studies for causal effects
- Causal diagrams for empirical research
- Inference and missing data
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Matching on the Estimated Propensity Score
- Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Multiple Imputation and its Application
- Balancing Covariates via Propensity Score Weighting
- Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
- Inference for imputation estimators
- Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
- Double/debiased machine learning for treatment and structural parameters
- Causal inference with confounders missing not at random
- Flexible Imputation of Missing Data, Second Edition
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Random forests
This page was built for publication: Doubly robust treatment effect estimation with missing attributes
