Propensity score weighting for causal inference with multi-stage clustered data
Shu Yang

TL;DR
This paper introduces a calibrated propensity score weighting method tailored for multi-stage clustered survey data, effectively addressing unmeasured cluster confounders and improving causal inference accuracy.
Contribution
It proposes a novel calibration approach that balances covariates and cluster effects, ensuring robustness even with unmeasured confounders and multiple treatments.
Findings
Estimator is consistent without correct propensity score model specification
Method outperforms existing approaches in simulations
Applied to school BMI data to assess obesity prevalence
Abstract
Propensity score weighting is a tool for causal inference to adjust for measured confounders. Survey data are often collected under complex sampling designs such as multistage cluster sampling, which presents challenges for propensity score modeling and estimation. In addition, for clustered data, there may also be unobserved cluster effects related to both the treatment and the outcome. When such unmeasured confounders exist and are omitted in the propensity score model, the subsequent propensity score adjustment will be biased. We propose a calibrated propensity score weighting adjustment for multi-stage clustered data in the presence of unmeasured cluster-level confounders. The propensity score is calibrated to balance design-weighted covariate distributions and cluster effects between treatment groups. In particular, we consider a growing number of calibration constraints increasing…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Statistical Methods and Bayesian Inference
