Method library WORKFLOW / 015

Research workflow/5 steps

Difference-in-differences with multiple periods (did)

@did ✓ P D · Jun 29, 2026 · 1.3k views

@misc{did,
  title        = {did},
  author       = {Callaway and Sant'Anna},
  howpublished = {\url{https://bcallaway11.github.io/did/}},
  note         = {Software / documentation}
}

In the margins · Peer notes@nuisance_nina · The not-yet-treated control group is what makes this robust to heterogeneous timing. Great default. 2

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 5 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Build the staggered panel

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

One row per (unit, period); never-treated units get group 0 / Inf.

Reads fromNot specified Feeds into#2

Key code

# id · period · first.treat (group) · Y
head(panel)

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

[did] Group-time ATT — att_gt()

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Estimate ATT(g,t) for every cohort and period against not-yet-treated controls.

Formula

\mathrm{ATT}(g,t)=\mathbb{E}\!\left[\,Y_t(g)-Y_t(0)\mid G=g\,\right]

The estimator

Group-time ATT — att_gt() — Difference-in-differences with multiple periods and staggered adoption: ATT(g,t) with clean (not-yet-treated) controls.

ATT Code @ HEAD ↗ Paper ↗

Reads from#1 Feeds into#3#4

Key code

att <- att_gt("Y", "period", "id", "first.treat", data = panel)

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Inference

aggte(type = 'dynamic')

Step notesField note & discussion1

Uncertainty quantification — standard errors, intervals, and aggregation.

What happens here

Aggregate to an event study — effect by length of exposure; pre-periods test parallel trends.

Reads from#2 Feeds into#5

Key code

es <- aggte(att, type = "dynamic")

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Heterogeneity

aggte(type = 'group')

Step notesField note & discussion1

Heterogeneity — who is affected, and by how much, not just on average.

What happens here

Average effect within each treatment cohort.

Reads from#2 Feeds into#5

Key code

aggte(att, type = "group")

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

ggdid() event-study plot

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

Plot pre-trends + dynamic effects with simultaneous confidence bands.

Reads from#3#4 Feeds intoNot specified

Key code

ggdid(es)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

A long panel with staggered treatment timing: unit id, period, the unit's first-treatment period (group), and an outcome.

Show data format & exampleHide example

Format — one row per (unit, period). first.treat = the period a unit is first treated (0 / Inf = never treated).

 id  period  first.treat   Y
  1     2004        2006    8.1
  1     2005        2006    8.4
  2     2004           0    7.9   # never treated

Read the evidence

Results & interpretation

4 figures Workflow discussion 2

01FigureGroup-time ATT(g,t) from att_gt() — the effect for each treatment cohort in each period.

02FigureRaw outcome paths by group — the picture before any aggregation.

03FigureEvent-study aggregation (aggte, dynamic): effect by length of exposure, with pre-period placebo checks.

04FigureGroup / calendar aggregations summarising the ATT a few different ways.

Figures reproduced from did — Callaway & Sant'Anna — unofficial community showcase; all credit to the original authors.

Context & interpretation

The flagship 'did' vignette (Callaway & Sant'Anna). Estimate ATT(g,t) against not-yet-treated controls, then aggregate to a dynamic event study. Unofficial summary.

Peer notes

Discuss the workflow (2)

4

@did_master · Jun 29, 2026

The whole point: never compare a treated unit to an already-treated one. att_gt() bakes that in. TWFE users, please switch.

4

@hte_hannah · Jun 29, 2026

And the dynamic aggregation gives you the event study for free, with simultaneous bands. Chef's kiss.
6

@nuisance_nina · Jun 29, 2026

The not-yet-treated control group is what makes this robust to heterogeneous timing. Great default.