#statstab #529 Understanding and misunderstanding randomized controlled trials

Thoughts: Randomization is close to magic, but we still must be critical of our methods.

#randomization #rct #design #critique #covariates #causalinference #ATE #confounder #bias

https://doi.org/10.1016/j.socscimed.2017.12.005

#statstab #523 Equal vs. Proportional Weighting in Least-Squares Means

Thoughts: Interesting comparisons of bias for the two methods. Complex stuff.

I wonder what people make of this.

#weighting #error #bias #LSM #emmeans #covariates

https://imazubi.github.io/stats/stats_equal_vs_proportional_lsm/index_equal_vs_proportional_lsm.html

#statstab #433 The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients

Thoughts: Adding predictors to your model results in marked changes in inference. Be mindful!

#table2 #fallacy #covariates #bias #inference #regression

https://doi.org/10.1093/aje/kws412

'Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds', by Haoshu Xu, Hongzhe Li.

http://jmlr.org/papers/v26/24-0493.html

#wasserstein #covariates #covariate

'Decorrelated Variable Importance', by Isabella Verdinelli, Larry Wasserman.

http://jmlr.org/papers/v25/22-0801.html

#regression #covariates #prediction

'Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance', by Ray Bai, Mary R. Boland, Yong Chen.

http://jmlr.org/papers/v24/20-1437.html

#lasso #mcmc #covariates