moderndid.ipw_did_panel#

moderndid.ipw_did_panel(y1, y0, d, covariates, i_weights=None, boot=False, boot_type='weighted', nboot=999, influence_func=False, trim_level=0.995)[source]#

Compute the inverse propensity weighted DiD estimator for the ATT with panel data.

Implements the inverse propensity weighted (IPW) estimator for the ATT with panel data, as proposed by [1] and discussed in [2]. The estimator is given by equation (2.4) in [2] as

\[\widehat{\tau}^{ipw,p} = \frac{1}{\mathbb{E}_{n}[D]} \mathbb{E}_{n} \left[\frac{D-\widehat{\pi}(X)}{1-\widehat{\pi}(X)}\left(Y_{1}-Y_{0}\right)\right].\]

IPW weights are not normalized to sum up to one, that is, the estimator is of the Horwitz-Thompson type.

Parameters:

y1numpy.ndarray: A 1D array of outcomes from the post-treatment period.
y0numpy.ndarray: A 1D array of outcomes from the pre-treatment period.
dnumpy.ndarray: A 1D array of group indicators (1 if treated in post-treatment, 0 otherwise).
covariatesnumpy.ndarray or None: A 2D array of covariates for propensity score estimation. An intercept must be included if desired. If None, leads to an unconditional DiD estimator.
i_weightsnumpy.ndarray, optional: A 1D array of observation weights. If None, weights are uniform. Weights are normalized to have a mean of 1.
bootbool, default=False: Whether to use bootstrap for inference.
boot_type{“weighted”, “multiplier”}, default=”weighted”: Type of bootstrap to perform.
nbootint, default=999: Number of bootstrap repetitions.
influence_funcbool, default=False: Whether to return the influence function.
trim_levelfloat, default=0.995: The trimming level for the propensity score.

Returns:

IPWDIDPanelResult: A NamedTuple containing the ATT estimate, standard error, confidence interval, bootstrap draws, and influence function.