Mathematics
8 years ago

Adjustment for Measurement Error in Covariates: Crucial for Accurate Causal Effects

Wiley series in probability and statistics
Adjustment when Covariates are Fallible
Steffi Pohl, Marie‐Ann Sengewald, Rolf Steyer

Paper Summary

Related papers

Theme background image
Mathematics
3 years ago

New method accurately predicts individual effects, revolutionizing personalized medicine.

Missing Data
Cultivar Evaluation
Clinical Trials
1 view
Theme background image
Biochemistry, Genetics and Molecular Biology
5 years ago

Revolutionizing genomics predictions: Paired lasso outperforms standard methods

Genomic Selection
Regularization Methods
Real-Time PCR
Theme background image
Mathematics
8 years ago

Adjustment for Measurement Error in Covariates: Crucial for Accurate Causal Effects

Causal Inference
Regularization Methods
Missing Data
1 view

The article discusses how to adjust for measurement errors in covariates in scientific research. It explores when adjustments should be based on the actual covariate data and when they should be based on more accurate hidden covariate information. The study shows that measurement errors in covariates can bias the estimation of causal effects. Different methods to correct for these errors are evaluated, considering the impact of additional covariates. The findings suggest that adjusting for latent covariates can improve the accuracy of causal effect estimates in empirical studies.