\begin{abstract}
We present a methodology for creating enhanced microsimulation datasets by combining the Current Population Survey (CPS) with the IRS Public Use File (PUF). Our two-stage approach uses quantile regression forests to impute 72 tax variables from the PUF onto CPS records, preserving distributional characteristics while maintaining household composition and member relationships. The imputation process alone does not guarantee consistency with official statistics, necessitating a reweighting step to align the combined dataset with known population totals and administrative benchmarks. We apply a reweighting algorithm that calibrates the dataset to over 7,000 targets from six sources: IRS Statistics of Income, Census population projections, Congressional Budget Office program estimates, Treasury expenditure data, Joint Committee on Taxation tax expenditure estimates, and healthcare spending patterns. The reweighting employs dropout-regularized gradient descent optimization to ensure consistency with administrative benchmarks. Validation shows the enhanced dataset reduces error in key tax components by [TO BE CALCULATED]\% relative to the baseline CPS. The dataset maintains the CPS's demographic detail and geographic granularity while incorporating tax reporting data from administrative sources. We release the enhanced dataset, source code, and documentation to support policy analysis.
\end{abstract}