StatsClaw: An AI-Collaborative Workflow for Statistical Software Development

Abstract

Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation -- a critical requirement for statistical software. We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation. A planning agent produces independent specifications for implementation, simulation, and testing, dispatching them to separate agents that cannot see each other's instructions: the builder implements without knowing the ground-truth parameters, the simulator generates data without knowing the algorithm, and the tester validates using deterministic criteria. We describe the approach, demonstrate it end-to-end on a probit estimation package, and evaluate it across three applications to the authors' own R and Python packages. The results show that structured AI-assisted workflows can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.

Figures (7)

• Figure 1: StatsClaw workflow architecture. The planner produces three isolated specification documents; the builder, tester, and simulator each receive only their own specification ($\times$ marks information barriers). The reviewer cross-compares all pipeline outputs before issuing a ship verdict.
• Figure 2: Monte Carlo comparison of three probit estimators across different sample sizes $N \in \{200, 500, 1000, 5000\}$ with 500 replications per scenario. Columns: MLE (blue), Gibbs (red), MH (green). Rows: $|\text{Bias}|$, RMSE, 95% CI coverage, computation time. All three methods exhibit consistency, $\sqrt{N}$-convergence, and nominal coverage---confirming that the C++ implementations match their mathematical specifications.
• Figure 3: Left: CEO--Firm bipartite network with 48 nodes, 5 connected components, and 11 singletons, reproducing the diagnostic in correia2016feasible. Right: three-way FE (unit $\times$ time $\times$ region) as a $k$-partite graph. Both produced by panelview(type = "network").
• Figure 4: Conditional marginal effect estimates from R interflex (left) and Python interflex (right) on the same DGP. Both recover the true conditional marginal effect $\partial E[Y|D,X]/\partial D = 2 + 1.5X$ with matching point estimates and confidence intervals.
• Figure 5: Component-wise relative error before and after the convergence fix ($\texttt{tol}=10^{-3}$). The old global criterion allowed 9.4% error in the factor component because the grand mean dominated the denominator. The new criterion ensures each component converges to its own scale.