moderndid.diddynamic.DynBalancingResult

class moderndid.diddynamic.DynBalancingResult(att: float, var_att: float, mu1: float, mu2: float, var_mu1: float, var_mu2: float, robust_quantile: float, gaussian_quantile: float, gammas: dict, coefficients: dict, imbalances: dict, estimation_params: dict = {})

Bases: NamedTuple

Container for dynamic covariate balancing treatment effect estimates.

Stores point estimates, variances, and diagnostic information produced by the dynamic covariate balancing estimator. The average treatment effect is defined as

\[\text{ATE} = \mu_1 - \mu_2\]

where \(\mu_1\) and \(\mu_2\) are the potential outcome estimates under the two treatment histories ds1 and ds2.

This class implements the maketables plug-in interface for publication-quality tables. See Publication Tables with maketables.

Attributes:

attfloat

The ATE point estimate (\(\mu_1 - \mu_2\)).

var_attfloat

Variance of the ATE.

mu1float

Potential outcome estimate under ds1.

mu2float

Potential outcome estimate under ds2.

var_mu1float

Variance of mu1.

var_mu2float

Variance of mu2.

robust_quantilefloat

Robust chi-squared critical value for inference.

gaussian_quantilefloat

Gaussian critical value for inference.

gammasdict

Balancing weights per treatment history (keys 'ds1', 'ds2').

coefficientsdict

LASSO coefficients per treatment history.

imbalancesdict

Covariate imbalance measures.

estimation_paramsdict

Standard moderndid metadata (observation count, variable names, etc.).

References

[1]

Viviano, D. and Bradic, J. (2026). “Dynamic covariate balancing: estimating treatment effects over time with potential local projections.” Biometrika, asag016. https://doi.org/10.1093/biomet/asag016

Methods

count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

Attributes

att	The ATE point estimate.
coefficients	LASSO coefficients per treatment history.
estimation_params	Standard moderndid metadata.
gammas	Balancing weights per treatment history.
gaussian_quantile	Gaussian critical value.
imbalances	Covariate imbalance measures.
mu1	Potential outcome estimate under ds1.
mu2	Potential outcome estimate under ds2.
robust_quantile	Robust chi-squared critical value.
se	Standard error of the ATE estimate.
var_att	Variance of the ATE.
var_mu1	Variance of mu1.
var_mu2	Variance of mu2.