py2sdid 0.1.0

Two-stage DiD and BJS imputation estimator for staggered treatment designs

py2sdid

Two-stage difference-in-differences (Gardner 2021) and BJS imputation estimator (Borusyak, Jaravel, Spiess 2024) for staggered treatment designs in Python, built on polars, scipy (sparse), and numpy.

Installation

pip install py2sdid

From source:

git clone https://github.com/AlanHuang99/py2sdid.git
cd py2sdid
pip install -e ".[dev]"

Quick Start

import polars as pl
import py2sdid

data = pl.read_parquet("panel_data.parquet")

result = py2sdid.ts_did(
    data,
    yname="dep_var",
    idname="unit_id",
    tname="year",
    gname="cohort_year",
    cluster_var="cluster_id",
)

result.summary()
result.plot(kind="event_study")
result.diagnose()

Estimators

ts_did -- Two-Stage DiD (Gardner 2021)

Estimates unit and time fixed effects on untreated observations only, residualizes the outcome for the full sample, and regresses on treatment indicators. Standard errors use GMM influence functions that correct for the generated-regressor problem.

bjs_did -- BJS Imputation (Borusyak, Jaravel, Spiess 2024)

Same first stage as ts_did. Directly averages residuals for treated observations rather than running a second-stage regression. Standard errors use the BJS imputation formula (Equations 6, 8, 10 of the paper).

Both produce identical point estimates. They differ in the analytic standard error formula. Both support cluster bootstrap as an alternative.

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Parameters

ts_did() / bjs_did()

ts_did(
    data, yname, idname, tname, gname,
    *, xformla=None, wname=None, cluster_var=None,
    anticipation=0, se=True, bootstrap=False,
    n_bootstraps=500, seed=None, n_jobs=None, verbose=True,
) -> DiDResult

Parameter	Type	Default	Description
data	pl.DataFrame	required	Panel data in long format (one row per unit per period)
yname	str	required	Outcome variable column
idname	str	required	Unit identifier column
tname	str	required	Time period column (integer-valued)
gname	str	required	Treatment cohort column (see below)
xformla	list[str]	None	Time-varying covariate columns for the first stage
wname	str	None	Observation weight column
cluster_var	str	idname	Column to cluster standard errors on
se	bool	True	Compute standard errors
bootstrap	bool	False	Use cluster bootstrap instead of analytic SEs
n_bootstraps	int	500	Number of bootstrap replications
seed	int	None	Random seed for bootstrap
n_jobs	int	CPU count	Parallel workers for bootstrap
verbose	bool	True	Print progress

The gname Column

The gname column encodes the treatment cohort for each unit — the time period when treatment begins.

• An integer value (e.g. 2000) means the unit first receives treatment in that period.
• 0 or null means the unit is never treated.
• All observations for a given unit should have the same gname value.

The estimator uses this to determine:

• Which observations are untreated (used in the first stage): all observations where tname < gname, plus all observations from never-treated units.
• Which observations are treated (used for treatment effect estimation): observations where tname >= gname.
• The relative time (event time) for each observation: tname - gname.

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Output

Both estimators return a DiDResult with these fields:

Field	Type	Description
att_avg	float	Overall average treatment effect on treated
att_avg_se	float	Standard error of overall ATT
att_avg_ci	tuple	95% confidence interval
att_avg_pval	float	p-value (H0: ATT = 0)
event_study	pl.DataFrame	Per-period estimates for all relative time periods
unit_fe	np.ndarray	Estimated unit fixed effects
time_fe	np.ndarray	Estimated time fixed effects
vcov	np.ndarray	Variance-covariance matrix

The event_study DataFrame contains columns: rel_time, estimate, se, ci_lower, ci_upper, pval, count. It includes all relative time periods present in the data, both pre-treatment and post-treatment.

Convenience properties:

• result.att_by_horizon — post-treatment rows only (rel_time >= 0)
• result.pretrend_tests — pre-treatment rows only (rel_time < 0)

Methods

Method	Description
summary()	Formatted text summary
plot(kind)	Matplotlib figure. Kinds: event_study, pretrends, treatment_status, counterfactual, honestdid, calendar
diagnose()	Pre-trend F-test, TOST equivalence, HonestDiD sensitivity

---

Data Format

Input: polars.DataFrame in long panel format.

Column	Type	Description
outcome	float	Outcome variable
unit id	int/str	Unit identifier
time	int	Time period (e.g. year)
cohort	int	Treatment cohort (0 or null = never-treated)

unit_id | year | cohort_year | dep_var
--------|------|-------------|--------
1       | 1990 | 2000        | 4.23
1       | 1991 | 2000        | 4.51
...
501     | 1990 | 0           | 3.82
501     | 1991 | 0           | 3.90

---

Testing

uv run pytest tests/                    # all tests
uv run pytest tests/test_vs_r.py -v -s  # R validation (requires R + did2s + didimputation)

Test data fixture in tests/data/ is generated deterministically via tests/data/generate_fixture.py.

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API Reference

• REFERENCE.md -- parameter tables, return types
• API docs -- searchable HTML reference (pdoc)

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References

• Gardner, J. (2021). "Two-Stage Differences in Differences." Working paper.
• Borusyak, K., Jaravel, X., & Spiess, J. (2024). "Revisiting Event-Study Designs: Robust and Efficient Estimation." Review of Economic Studies, 91(6), 3253-3285.
• Rambachan, A. & Roth, J. (2023). "A More Credible Approach to Parallel Trends." Review of Economic Studies, 90(5), 2555-2591.
• Butts, K. & Gardner, J. (2022). "did2s: Two-Stage Difference-in-Differences." The R Journal, 14(1).

License

MIT