diff_diff.ContinuousDiD#

class diff_diff.ContinuousDiD[source]#

Bases: object

Continuous Difference-in-Differences estimator.

Implements the methodology from Callaway, Goodman-Bacon & Sant’Anna (2024) for estimating dose-response curves when treatment has a continuous intensity.

Parameters:

  • degree (int, default=3) – B-spline degree (3 = cubic).

  • num_knots (int, default=0) – Number of interior knots for the B-spline basis.

  • dvals (array-like, optional) – Custom dose evaluation grid. If None, uses quantile-based default.

  • control_group (str, default="never_treated") – "never_treated", "not_yet_treated", or "lowest_dose". "lowest_dose" implements Remark 3.1 (CGBS 2024) for settings with no never-treated / zero-dose units (P(D=0) = 0): the lowest-dose group d_L becomes the comparison and the estimand is ATT(d) − ATT(d_L). Requires a genuine lowest-dose group (>= 2 units at d_L, i.e. P(D=d_L) > 0) and no never-treated units present. Single-cohort only (multi-cohort and covariates= raise NotImplementedError).

  • anticipation (int, default=0) – Number of periods of treatment anticipation.

  • base_period (str, default="varying") – "varying" or "universal".

  • alpha (float, default=0.05) – Significance level for confidence intervals.

  • n_bootstrap (int, default=0) – Number of multiplier bootstrap iterations. 0 for analytical SEs only.

  • bootstrap_weights (str, default="rademacher") – Bootstrap weight type: "rademacher", "mammen", or "webb".

  • seed (int, optional) – Random seed for reproducibility.

  • rank_deficient_action (str, default="warn") – Action for rank-deficient B-spline OLS: "warn", "error", or "silent".

  • covariates (list of str, optional) – Column names of covariates for conditional parallel trends (E[ΔY(0) | D=d, X] = E[ΔY(0) | D=0, X]). When None (default) the estimator uses unconditional parallel trends. Covariates enter through a covariate-adjusted per-cell control counterfactual (see estimation_method). Covariates are read from the base period of each (g, t) comparison. Not currently composable with survey_design= (raises NotImplementedError).

  • estimation_method (str, default="dr") – Covariate-adjustment method (only used when covariates is set): "reg" (outcome regression) or "dr" (doubly-robust, the default). "ipw" is not supported on the dose / event-study aggregation — pure IPW’s covariate adjustment is a single scalar level shift, so it cannot adjust the dose-response shape (ACRT(d) would be identical to the unconditional fit); it raises NotImplementedError. reg and dr share the dose-response shape and ACRT(d); dr differs only in the overall_att / ATT(d) level and in its doubly-robust standard errors.

  • pscore_trim (float, default=0.01) – Propensity-score trimming bound for the dr path (scores clipped to [pscore_trim, 1 - pscore_trim]).

  • epv_threshold (float, default=10.0) – Events-per-variable threshold for the dr propensity logit diagnostics.

  • pscore_fallback (str, default="error") – Action when dr propensity estimation raises (the logit IRLS fails with a LinAlgError / ValueError, e.g. perfect separation or rank deficiency): "error" (re-raise — the default, fail-closed so a dr fit never silently degrades to a non-DR estimate) or "unconditional" (fall back to an unconditional propensity with a warning; the affected cells are then reg-like — use only when you knowingly accept that). Note: low events-per-variable emits a diagnostic warning but does not itself trigger the fallback.

  • treatment_type (str, default="continuous") – Dose-response model: "continuous" (B-spline sieve, the default) or "discrete" (saturated per-dose-level regression, CGBS 2024 Eq. 4.1). On the discrete path each distinct dose level gets its own effect coefficient — ATT(d_j) = mean_{D=d_j}(ΔY) − control (a per-level 2×2 DiD) — and ACRT(d_j) is the paper’s backward finite difference on the grid {0, d_1, ..., d_J} (ACRT(d_1) = ATT(d_1)/d_1, so a binary dose D in {0, 1} gives ACRT = ATT). It composes with covariates and survey_design and reduces to the per-level 2×2 DiD standard error. Multi-cohort fits must share the same dose support across cohorts (else NotImplementedError); an off-support dvals value raises ValueError.

Examples

>>> from diff_diff import ContinuousDiD, generate_continuous_did_data
>>> data = generate_continuous_did_data(n_units=200, seed=42)
>>> est = ContinuousDiD(n_bootstrap=199, seed=42)
>>> results = est.fit(data, outcome="outcome", unit="unit",
...                   time="period", first_treat="first_treat",
...                   dose="dose", aggregate="dose")
>>> results.overall_att

Methods

__init__([degree, num_knots, dvals, ...])

fit(data, outcome, unit, time, first_treat, dose)

Fit the continuous DiD estimator.

get_params()

Return estimator parameters as a dictionary.

set_params(**params)

Set estimator parameters and return self.
__init__(degree=3, num_knots=0, dvals=None, control_group='never_treated', anticipation=0, base_period='varying', alpha=0.05, n_bootstrap=0, bootstrap_weights='rademacher', seed=None, rank_deficient_action='warn', covariates=None, estimation_method='dr', pscore_trim=0.01, epv_threshold=10.0, pscore_fallback='error', treatment_type='continuous')[source]#
Parameters:

  • degree (int)

  • num_knots (int)

  • dvals (ndarray | None)

  • control_group (str)

  • anticipation (int)

  • base_period (str)

  • alpha (float)

  • n_bootstrap (int)

  • bootstrap_weights (str)

  • seed (int | None)

  • rank_deficient_action (str)

  • covariates (List[str] | None)

  • estimation_method (str)

  • pscore_trim (float)

  • epv_threshold (float)

  • pscore_fallback (str)

  • treatment_type (str)

classmethod __new__(*args, **kwargs)#