didint: A Stata-Julia tool for intersection difference in differences

I introduce didint, a Stata wrapper for the Julia-based DiDInt.jl package, which implements a recent extension of the interaction difference-in-differences estimator to account for covariates. The method, proposed by Karim and Webb (2024), addresses bias that can arise when adjusting for covariates in staggered adoption settings—especially when the common causal covariates (CCC) assumption is violated. didint estimates the average treatment effect on the treated (ATET) by applying a regression-based residualization approach that allows for state-level, time-level, or fully interacted (state-by-time) varying controls. The command supports multiple specifications via the ccc() option, enabling researchers to flexibly compare assumptions about the role of covariates in the data-generating process. In addition to covariate handling, didint implements both a cluster jackknife and a randomization inference procedure, allowing users to construct cluster–robust p-values, especially when few units are treated or treatment timing is concentrated. The interface is designed for flexibility in applied research: users specify outcome, time, and state variables, treatment timing, and optional control variables. Additional options include custom time frequencies, automatic cohort length adjustment, and full compatibility with panel data. Estimates and inference results (ATETs, standard errors, and p-values) are returned directly to the active Stata dataset for immediate use. This presentation will briefly describe the underlying methodology and demonstrate its application to empirical examples. didint equips Stata users with a robust and principled framework for conducting difference-in-differences analyses with covariates in staggered treatment designs.