undid: A Stata package for difference in differences with unpoolable data

I introduce a Stata package for the difference-in-differences estimator for unpoolable data (UN-DID), designed for settings where data from treatment and control units are partitioned across silos that cannot be pooled because of legal, technical, or institutional constraints. UN-DID estimates the average treatment effect on the treated (ATET) by computing within-silo pre- and post differences and aggregating these across silos. The estimator is unbiased under parallel trends across silos and accommodates both common and staggered adoption. For inference, I implement a randomization-based procedure and a jackknife standard-error estimator that remains valid under treatment-effect heterogeneity and variation in treatment timing. To support implementation in applied work, I developed a three-stage Stata interface (undid) for executing the UN-DID procedure. Users begin by specifying unit identifiers and treatment timings in a simple initialization file. The software then prepares the required difference calculations within each silo, followed by a final estimation stage that calculates ATETs and computes p-values. The interface supports optional covariate adjustment and produces descriptive statistics and identification diagnostics. The Stata package is designed for collaborative environments where direct pooling of data across treatment and control units is not possible, such as multijurisdictional research settings or contexts with confidentiality constraints.