moderndid.ipw_did_rc#

moderndid.ipw_did_rc(y, post, 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 repeated cross-section data.

Implements the inverse propensity weighted (IPW) estimator for the ATT with repeated cross-section data, as proposed by [1] and discussed in [2]. The estimator is given by the sample analogue of equation (2.3) in [2] as

\[\tau = \frac{1}{\mathbb{E}[D]} \mathbb{E}\left[\frac{D-p(X)}{1-p(X)} \frac{T-\lambda}{\lambda(1-\lambda)} Y\right].\]

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

Parameters:

ynumpy.ndarray: A 1D array of outcomes from both pre- and post-treatment periods.
postnumpy.ndarray: A 1D array of post-treatment dummies (1 if post-treatment, 0 if pre-treatment).
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:

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