Source
did — Callaway & Sant'Anna
@misc{did,
title = {did},
author = {Callaway and Sant'Anna},
howpublished = {\url{https://bcallaway11.github.io/did/}},
note = {Software / documentation}
}Summary by StatsDoge
Difference-in-differences with multiple periods and staggered adoption: ATT(g,t) with clean (not-yet-treated) controls.
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https://github.com/bcallaway11/did
unpinned — link may rot
⚠️ Unofficial community write-up of did. This account is not affiliated with the authors; it summarizes the public documentation for demonstration. All credit & copyright belong to the original authors.
What it does
Estimates group-time average treatment effects ATT(g, t) for every treatment cohort g and period t, then aggregates them into dynamic (event-study), group, or calendar summaries — avoiding the negative-weighting problems of two-way fixed effects under staggered adoption.
library(did)
att <- att_gt(yname = "Y", tname = "period", idname = "id",
gname = "first.treat", data = panel)
aggte(att, type = "dynamic")
Used in these workflows (1)
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Difference-in-differences with multiple periods (did)
Staggered-adoption DiD done right: group-time ATT(g,t) → event-study / group / calendar aggregations, with honest pre-trends.
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