csdid news

csdid 2.0.0

A rewritten estimation engine. Everything below is what changes for someone upgrading from csdid Version 1.82 — the version SSC distributes today via ssc install csdid, dated 2025-10-05. The command surface is deliberately the same, so most existing do-files run unchanged.

Changes that can affect your results

Not-yet-treated is now the default comparison group. Version 1.82 and the reference implementation both default to never-treated. Version 2.0.0 uses every unit not yet treated at t as a control. It uses more of the data, usually gives tighter standard errors, and does not depend on a never-treated group existing or being large enough to trust. nevertreated restores the old behaviour.

One consequence: the refusal described below, when the never-treated group is too small, no longer fires by default. That is correct — notyet is precisely the remedy that refusal recommends.

Universal base period is now the default. Version 1.82 and the reference implementation both default to a varying base period. Version 2.0.0 measures every cell against g-1.

This is the layout an event-study plot assumes, and event studies are how these results are nearly always presented. Post-treatment effects are identical under either choice; only the pre-treatment cells differ, and universal additionally reports the g-1 normalisation row. Use base_period(varying) when pre-testing: each pre-treatment cell is then its own one-period comparison, so a violation shows up in the period where it happens rather than being carried forward into every later cell.

Both of these are deliberate, documented departures from the reference implementation as well as from Version 1.82.

Standard errors are bootstrapped by default, with simultaneous confidence bands. Version 1.82 reported pointwise analytical standard errors unless you asked for wboot.

This is deliberate. A staggered design produces one estimate per cohort and period — often dozens — and pointwise intervals do not account for looking at all of them at once. Reading a 95% pointwise band as though it covered the whole event study understates uncertainty, and it is the most common way these results are over-read. The default is now the multiplier bootstrap with simultaneous bands over 1,000 iterations, so the interval you are shown is the one that covers every reported effect jointly.

analytical (or vce(analytical)) restores pointwise analytical standard errors, and pointwise gives pointwise intervals from the bootstrap. Point estimates are unaffected by any of this.

Unbalanced panels are balanced, and say so. Version 1.82 dropped, without comment, the units not observed in both periods of each comparison — silently changing the estimand. Version 2.0.0 makes the choice explicit and reports it. bal() takes three modes:

Whenever a mode discards observations, csdid reports how many units and how many observations went. e(panel_mode) records the resolved layout. unbalanced is a supported synonym of bal(none), for when that reads better than a mode inside bal(); allowunbalanced and allow_unbalanced are the longhand form of the same thing, carrying the name the reference implementation gives this setting. All three are typed in full — no abbreviation of them is an option.

Repeated cross sections can be declared, not just inferred. The new rcs option is the counterpart of the reference implementation’s panel = FALSE. Previously the only way to say “these are cross sections” was to omit ivar(), which forced anyone whose cross sections carried an identifier to withhold a real variable. With rcs you keep it: it is validated and used to exclude observations where it is missing, but each observation is its own unit. cluster() is what puts that identifier back into the standard errors.

A too-small never-treated group is now refused. csdid stops when the never-treated group is smaller than #covariates + 5, and warns about any small group. Group size is measured as rows divided by periods — the average number of units per period — not as distinct units. The two agree on balanced panels and differ only on unbalanced ones, where the guard now fires in cases earlier versions estimated. If it fires, notyet uses not-yet-treated units as the comparison group and does not depend on the never-treated group being large. This changes whether the command runs, never an estimate.

Stored results

e() carries the estimation contract; unit-level objects stay internal. The influence functions — one row per unit, one column per ATT(g,t) — drive every standard error, aggregation, and bootstrap, and they live inside the estimation engine. Every feature computes from that internal copy: estat, csdid_stats, test/lincom (with full covariances), csdid_plot, and saverif(). This is the same division of labor official Stata commands use for unit-level quantities, and it is what keeps large estimations fast: copying an n-unit matrix into e() costs quadratic time in the number of units. Two explicit routes expose the influence functions when you want them — storeall materializes e(inffunc), e(unit_group), and e(cluster_vec) as Stata matrices, and saverif() writes the durable dataset that csdid_stats using aggregates in any later session.

Options that now error instead of being accepted quietly

New

• Repeated cross sections. Omit ivar(), or declare them with rcs and keep the identifier for cluster().
• Clustered standard errors without the bootstrap. cluster() with analytical reports cluster-robust standard errors at every aggregation level, and the parallel-trends pre-test under clustering.
• fix_weights() — control how time-varying sampling weights are resolved in each 2×2 comparison: varying, base_period, or first_period.
• Parallel-trends pre-test reported with the results and stored in e(wald_stat), e(wald_pvalue) and e(wald_df).
• Influence functions on request — storeall materializes them as e(inffunc) for sensitivity analysis or custom aggregation, and saverif() writes them as a dataset that csdid_stats using aggregates later, in another session or on another machine.
• estat event, estat group, estat calendar, estat simple, estat dynamic and estat attgt as conventional postestimation forms.
• saving() on every estat subcommand, which writes what that subcommand computed to a dataset — the same option margins, simulate and graph take, so there is no separate export command.
• csdid_plot, saving() exports plot-ready data so you keep control of the graph styling.
• Transformation and factor covariates in the covariate list.
• No external dependencies. Version 1.82 required drdid from SSC; 2.0.0 requires nothing beyond Stata itself.

Performance

Version 2.0.0 is a rewritten engine, and speed at scale was a design goal alongside numerical parity with the reference implementation. Absolute numbers from one machine (Stata MP, Apple Silicon), so you can calibrate:

• A 400,000-observation repeated cross section with 20 periods and 12 cohorts estimates in under 30 seconds; a 350,000-row panel in one to three seconds for every method (dr, reg, ipw), with or without covariates.
• Aggregations never disturb stored results and are effectively instant at any size: estat event takes a fraction of a second after a 20,000-unit estimation and a few seconds after 400,000 units.
• The multiplier bootstrap is accelerated by a compiled plugin on macOS (shipped with the package; Mata everywhere else, with identical results): 199 replications on a 350,000-row panel add about two seconds.
• saverif() writes its dataset in about a second at 20,000 units, and cost scales linearly.

The design rule behind these numbers: no object with one row per unit ever crosses into Stata’s classic-matrix layer, whose cost is quadratic in a matrix’s longest dimension. Unit-level results live in the engine, in variables, or in files — never in e() matrices, unless you ask with storeall.

Legacy commands

csgvar (and its helper _gcsgvar) is carried forward and supported: it builds the gvar() cohort variable from a treatment indicator.

csdid_rif, csdid_table, dipt and tsvmat still ship so existing do-files keep running, but are deprecated and will be removed in a future release. Each prints a notice when called. They are not covered by the numerical test suite. help csdid_legacy documents what to use instead — in short, estat attgt, saving() for a results dataset, with the saved-RIF path still supported through csdid_stats using.

Compatibility

These are accepted and map to the documented spelling. Most warn; the ones marked as supported below do not, because they are current names rather than deprecations. New code should use the names in help csdid.

Spellings that are not options

These have never been options in any release, so there is nothing to be compatible with: they are absent from Version 1.82 and 2.0.0 is the first release of this rewrite. Each is refused as an unknown option (return code 198).

unbalanced and allow_unbalanced are not in this table: as options they are supported (see above). What is refused is unbalanced or allow_unbalanced as a value inside bal(). e(allow_unbalanced) and the allow_unbalanced value of e(panel_mode) are stored-result names and are unaffected.

Upgrading

The migration guide in the repository (docs/legacy-migration-guide.md) covers the migration in full, including how to compare Version 1.82 and 2.0.0 output on your own data.

Development history before this release is in the git log.