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Found 5 papers, 3 papers with code
Large language models improve Alzheimer's disease diagnosis using multi-modality data
In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference.
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees.
Causal Inference in Possibly Nonlinear Factor Models
This paper develops a general causal inference method for treatment effects models with noisily measured confounders.
On Binscatter
Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing.
Binscatter Regressions
The first four commands implement point estimation and uncertainty quantification (confidence intervals and confidence bands) for canonical and extended least squares binscatter regression (binsreg) as well as generalized nonlinear binscatter regression (binslogit for Logit regression, binsprobit for Probit regression, and binsqreg for quantile regression).
