Feols.ccv

ccv(treatment, cluster=None, seed=None, n_splits=8, pk=1, qk=1)

Compute the Causal Cluster Variance following Abadie et al (QJE 2023).

Parameters

Name	Type	Description	Default
treatment		The name of the treatment variable.	required
cluster	str	The name of the cluster variable. None by default. If None, uses the cluster variable from the model fit.	`None`
seed	int	An integer to set the random seed. Defaults to None.	`None`
n_splits	int	The number of splits to use in the cross-fitting procedure. Defaults to 8.	`8`
pk	float	The proportion of sampled clusters. Defaults to 1, which corresponds to all clusters of the population being sampled.	`1`
qk	float	The proportion of sampled observations within each cluster. Defaults to 1, which corresponds to all observations within each cluster being sampled.	`1`

Returns

Name	Type	Description
	pd.DataFrame	A DataFrame with inference based on the “Causal Cluster Variance” and “regular” CRV1 inference.

Examples

import pyfixest as pf
import numpy as np

data = pf.get_data()
data["D"] = np.random.choice([0, 1], size=data.shape[0])

fit = pf.feols("Y ~ D", data=data, vcov={"CRV1": "group_id"})
fit.ccv(treatment="D", pk=0.05, qk=0.5, n_splits=8, seed=123).head()

	Estimate	Std. Error	t value	Pr(>\|t\|)	2.5%	97.5%
CCV	-0.09017884663368153	0.206357	-0.437003	0.667307	-0.52372	0.343362
CRV1	-0.090179	0.108273	-0.832882	0.415826	-0.317652	0.137295