Implements the permutation-based conformal inference procedure of Chernozhukov, Wuthrich & Zhu (2021, JASA). The test inverts a sharp null \(H_0: \tau = \tau_0\) by imputing the treated post-treatment counterfactual as \(Y_{1t} - \tau_0\), re-estimating the counterfactual proxy on all \(T\) periods (imposing the null), and computing a moving-block permutation p-value from the estimated residuals. A confidence interval is obtained by test inversion over a grid of candidate \(\tau_0\).
Usage
conformal_inference(
fit,
tau0 = 0,
q = 1,
alternative = c("two.sided", "greater", "less"),
ci = TRUE,
level = 0.95,
grid = NULL,
n_grid = 200L,
grid_mult = 4,
...
)Arguments
- fit
A
coresynthobject fromscm_fit().- tau0
Null value of the ATT for the reported p-value (default 0).
- q
Exponent of the \(S_q\) test statistic (
S_q = (T_post^{-1} \sum |u_t|^q)^{1/q}). Default 1, robust to heavy-tailed data (CWZ 2021). Used only foralternative = "two.sided"; one-sided tests use the signed mean post-treatment residual.- alternative
"two.sided"(default),"greater", or"less".- ci
Logical; construct a confidence interval by test inversion (default
TRUE).- level
Confidence level for the interval (default 0.95).
- grid
Optional numeric vector of candidate \(\tau_0\) values for test inversion. When
NULL(default), a grid ofn_gridpoints is centred on the point estimate with half-widthgrid_multtimes the pre-treatment residual standard deviation.- n_grid
Number of grid points when
grid = NULL(default 200).- grid_mult
Half-width multiplier when
grid = NULL(default 4).- ...
Unused.
Value
A list of class c("conformal_inference", "coresynth_inference")
with estimate, se (NA; conformal has no SE), p_value (at tau0),
ci_lower, ci_upper, method ("conformal"), n_controls,
alternative, staggered (FALSE), plus tau0, q, grid, and
p_grid (p-values along the grid). Compatible with tidy() / glance().
Details
Supported for sharp (single-cohort) fits with method in
c("scm", "sdid", "gsc", "mc", "si"). Staggered, multi-arm, and tasc
fits are not supported (use sdid_inference(), gsc_inference(), or
si_inference() instead).