Handling parametric assumptions in principal causal effect estimation using Gaussian mixtures
From MaRDI portal
Publication:6628614
DOI10.1002/sim.9401zbMATH Open1547.62292MaRDI QIDQ6628614
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Gaussian mixturesnonparametric identificationcausal inferenceprincipal stratificationmoving exclusion restrictionparametric mixture modeling
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bayesian inference for causal effects in randomized experiments with noncompliance
- An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs
- Estimating the dimension of a model
- Identification of causal effects within principal strata using auxiliary variables
- Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program
- Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance
- Principal Stratification in Causal Inference
- Statistical analysis of finite mixture distributions
- Estimating Outcome Distributions for Compliers in Instrumental Variables Models
- Assessing the effect of an influenza vaccine in an encouragement design
- Improving inference of Gaussian mixtures using auxiliary variables
- Principal Stratification Analysis Using Principal Scores
Related Items (1)
This page was built for publication: Handling parametric assumptions in principal causal effect estimation using Gaussian mixtures