sdidlab

Specify, run, store and plot synthetic difference-in-differences models

In development. The package, documentation and a hosted demo app are being prepared for public release.

For my higher education research, I needed synthetic control and synthetic difference-in-differences (SDID) models with a single treated unit, and I needed to compare dozens of specifications without losing track of which result came from which choices. sdidlab is a tool I built around the synthdid estimator.

Features of sdidlab:

• Declarative specifications. A model is described in YAML (outcome, window, donor pool, covariates) and hashes to a stable identifier.
• Stored Runs. Every fit is saved with its full fitted object, placebo draws and provenance, so it can be reloaded and re-plotted without refitting.
• Inference for one treated unit. Provides trimmed placebo inference and RMSPE-ratio p-values instead.
• Diagnostics. Trajectory, donor and time weights, donor influence, placebo distributions, gap paths and event studies, styled after the Stata sdid package.
• Codegen. A specification can be turned into a runnable, committed R script.

The public release will include a demo app built on the California Proposition 99 data, where you can build a specification and download it as YAML plus an R script.