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This is the canonical reference for did_multiplegt_stat, formatted to mirror the Stata help file. For the Python API specifically (methods, attributes of DIDMultiplegtStat), see the Python API section.

Title

did_multiplegt_stat — Estimation of heterogeneity-robust difference-in-differences (DID) estimators, with a binary, discrete, or continuous treatment or instrument, in designs with stayers, assuming that past treatments do not affect the current outcome.

Syntax

from did_multiplegt_stat import DIDMultiplegtStat

model = DIDMultiplegtStat(
    estimator=...,           # str or list: "as" / "was" / "iv-was"
    order=1,                 # int, list[1] | list[4] | list[8]
    exact_match=False,
    noextrapolation=False,
    placebo=0,
    switchers=None,          # None | "up" | "down"
    disaggregate=False,
    as_vs_was=False,
    by=None,                 # list[str]
    by_fd=None,              # int
    by_baseline=None,        # int
    other_treatments=None,   # list[str]
    controls=None,           # list[str]
    weight=None,             # str
    cluster=None,            # str
    cross_fitting=0,
    trimming=0,
    on_placebo_sample=False,
    bootstrap=0,
    seed=0,
    twfe=False,              # bool | {"same_sample": True, "percentile": True, ...}
    cross_validation=None,   # {"algorithm": "kfolds", "max_k": 5, ...}
    asinstata=False,         # True for Stata-faithful regressions
    iv_method="manual",      # "manual" | "linearmodels" | "econtools"
    model_deltay=None,       # custom sklearn-style regressor
    model_stayer=None,       # custom sklearn-style classifier
)
model.fit(df, Y="...", ID="...", Time="...", D="...", Z=None)

from did_multiplegt_stat import did_multiplegt_stat

res = did_multiplegt_stat(df, Y, ID, Time, D, Z=None,
                          estimator=..., placebo=3, ...)

[bysort varlist:] did_multiplegt_stat Y G T D [Z] [if] [in] ///
    [, estimator(string) exact_match as_vs_was ///
       order(#/####/########) cross_fitting(#) ///
       controls(varlist) weights(varname) cluster(varlist) ///
       switchers(string) placebo(#) on_placebo_sample ///
       twfe(twfe_suboptions) noextrapolation trimming(#) ///
       other_treatments(varlist) by_fd(#) by_baseline(#) ///
       disaggregate graph_off bys_graph_off bootstrap(#) seed(#)]
Stata positional Python kwarg Meaning
Y Y= Outcome variable name
G ID= Unit identifier
T Time= Time period
D D= Treatment variable
Z (optional) Z= Instrumental variable

Description

Data and design

The command uses panel data at the (G, T) level to estimate heterogeneity-robust DID estimators, with a binary, discrete, or continuous treatment (or instrument). The command can be used in designs where there is at least one pair of consecutive time periods between which the treatment of some units, the switchers, changes, while the treatment of some other units, the stayers, does not change.

Target parameters

The command can estimate the Average Slope (AS) and the Weighted Average Slope (WAS) parameters introduced in de Chaisemartin et al. (2025).

  • The AS is the average, across switchers, of \(\big(Y_t(D_t) - Y_t(D_{t-1})\big) / (D_t - D_{t-1})\), the effect on their period-\(t\) outcome of moving their period-\(t\) treatment from its period-\((t-1)\) to its period-\(t\) value, scaled by the difference between these two values.
  • The WAS is a weighted average of switchers' slopes \(\big(Y_t(D_t) - Y_t(D_{t-1})\big) / (D_t - D_{t-1})\), where slopes receive a weight proportional to \(|D_t - D_{t-1}|\), switchers' absolute treatment change from period \((t-1)\) to period \(t\).

The variance of the WAS estimator is often smaller than that of the AS estimator, especially when there are switchers that experience a small treatment change.

Assumptions

When the data has more than two time periods, the command assumes a static model: units' outcome at period \(t\) only depends on their period-\(t\) treatment, not on their lagged treatments. See the did_multiplegt_dyn command for estimators allowing for dynamic effects.

The command also makes a parallel-trends assumption: the counterfactual outcome evolution switchers would have experienced if their treatment had not changed is assumed to be equal to the outcome evolution of stayers with the same baseline treatment. Importantly, this parallel-trends assumption is conditional on the baseline treatment: comparing switchers and stayers with different baseline treatments would implicitly amount to assuming that the treatment's effect is constant over time.

To test the parallel trends assumption underlying the estimators, the command can compute placebo estimators comparing the outcome evolution of switchers and stayers with the same baseline treatment before switchers' treatment changes.

Estimators, when exact_match is specified

With a binary or discrete treatment, if the exact_match option is specified, the estimators computed by the command compare the outcome evolution of switchers and stayers with the same period-\((t-1)\) treatment. Then, the WAS estimator computed by did_multiplegt_stat is numerically equivalent to the \(\text{DID}_M\) estimator proposed by de Chaisemartin and D'Haultfœuille (2020), and already computed by the did_multiplegt_old command.

did_multiplegt_stat uses an analytic formula to compute the estimator's variance, while did_multiplegt_old uses the bootstrap. Thus, the run time of did_multiplegt_stat is typically much lower.

The exact_match option can only be specified when the treatment is binary or discrete: with a continuously distributed treatment, one cannot find switchers and stayers with the exact same period-\((t-1)\) treatment. With a discrete treatment taking a large number of values, specifying this option may be undesirable — there may only be few switchers that can be matched to a stayer with the exact same period-\((t-1)\) treatment, thus restricting the estimation sample.

Estimators, when exact_match is not specified

When the exact_match option is not specified, the command computes a doubly-robust estimator, that combines regression adjustment and propensity-score reweighting to compare switchers and stayers controlling for their period-\((t-1)\) treatment.

  • The regression adjustment amounts to regressing, for all \(t\), \(Y_t - Y_{t-1}\) on a polynomial in \(D_{t-1}\) in the sample of \((t-1)\)-to-\(t\) stayers, and using the regression to predict switchers' \(Y_t - Y_{t-1}\).
  • Propensity score reweighting is based on logistic regressions of an indicator for \((t-1)\)-to-\(t\) switchers on a polynomial in \(D_{t-1}\).

Instrumental-variable case

There may be instances where the parallel-trends assumption fails, but one has at hand an instrument satisfying a similar parallel-trends assumption. For instance, one may be interested in estimating the price-elasticity of a good's consumption, but prices respond to supply and demand shocks, and the counterfactual consumption evolution of units experiencing and not experiencing a price change may therefore not be the same. On the other hand, taxes may not respond to supply and demand shocks and may satisfy a parallel- trends assumption.

In such cases, the command can compute the IV-WAS estimator introduced in de Chaisemartin et al. (2025) using doubly-robust estimators. The IV-WAS estimator is equal to the WAS estimator of the instrument's reduced-form effect on the outcome controlling for \(D_{t-1}\), divided by the WAS estimator of the instrument's first-stage effect on the treatment controlling for \(D_{t-1}\). See the paper for some explanations as to why controlling for \(D_{t-1}\) is desirable in IV estimation.

Notes

  1. The Stata command is compatible with estout. The Python class exposes .to_dataframe() and .get_coefficients(), which integrate with any pandas-aware reporting toolkit.
  2. The Stata command is byable (bysort varlist:). In Python this is the by=[...] keyword on DIDMultiplegtStat and on did_multiplegt_stat().

Authors

  • Clément de Chaisemartin, Economics Department, Sciences Po, France
  • Diego Ciccia, Sciences Po, France
  • Xavier D'Haultfœuille, CREST-ENSAE, France
  • Felix Knau, Sciences Po, France
  • Felix Pasquier, CREST-ENSAE, France
  • Doulo Sow, Sciences Po, France
  • Gonzalo Vazquez-Bare, UCSB, USA

Python port: Anzony Quispe.

Contact: chaisemartin.packages@gmail.com

References

  • de Chaisemartin, C., D'Haultfœuille, X., Pasquier, F., Sow, D., Vazquez-Bare, G. (2024) Difference-in-Differences for Continuous Treatments and Instruments with Stayers.
  • de Chaisemartin, C., D'Haultfœuille, X. (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.
  • de Chaisemartin, C., D'Haultfœuille, X. (2021) Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments.
  • Li, S., Linn, J., Muehlegger, E. (2014) Gasoline Taxes and Consumer Behavior.