Federated causal inference in heterogeneous observational data
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Publication:6149278
DOI10.1002/sim.9868arXiv2107.11732OpenAlexW3183697928MaRDI QIDQ6149278
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Publication date: 4 March 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.11732
Cites Work
- Doubly Robust Estimation in Missing Data and Causal Inference Models
- Causal inference using invariant prediction: identification and confidence intervals
- Inverse probability weighted estimation for general missing data problems
- The central role of the propensity score in observational studies for causal effects
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks
- Heterogeneity-aware and communication-efficient distributed statistical inference
- Anchor Regression: Heterogeneous Data Meet Causality
- Communication-Efficient Distributed Statistical Inference
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Maximum Likelihood Estimation of Misspecified Models
- Variance estimation in inverse probability weighted Cox models
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