diff_diff.RDDensityTestResult#

class diff_diff.RDDensityTestResult[source]#

Bases: Diagnostic

Results of the CJM 2020 manipulation (density-discontinuity) test.

The headline t_stat/p_value are the ROBUST BIAS-CORRECTED test (order-q density estimators at order-p-optimal bandwidths), with the standard error selected by vcov_type - when q > p; a fit with q == p makes the headline the CONVENTIONAL (uncorrected) test, which over-rejects at data-driven bandwidths (see the estimator class docstring). The *_conventional fields carry the same caveat. The f_* fields are WHOLE-SAMPLE-scale densities exactly as R’s hat$left/right/diff report them (joint-basis estimates). Per-side CONDITIONAL densities are these times (N-1)/(n_left-1) and (N-1)/(n_right-1) - the exact conversion under the rank-based EDF (0:(N-1))/(N-1) that R (and this port) implement; the CJM 2020 paper’s n/n_side factors apply to the paper’s (1/n)*sum EDF, which is not what R computes.

There are deliberately no att/se/conf_int fields: R rddensity reports no confidence interval anywhere in its output (intervals belong to rdplotdensity), and the estimand vocabulary of the treatment-effect estimators does not apply to a density test.

Methods

`__init__`(t_stat, p_value, f_left, f_right, ...)
`summary`()	R-style summary block.
`to_dataframe`()	Tidy per-side table: rows `left`/`right`/`diff` (plus the `*_conventional` rows when `report_all=True`); columns `estimate`/`se`/`t_stat`/`p_value` (inference populated on the `diff` rows only).

Attributes

`t_stat`
`p_value`
`f_left`
`f_right`
`f_diff`
`se_left`
`se_right`
`se_diff`
`f_left_conventional`
`f_right_conventional`
`f_diff_conventional`
`se_left_conventional`
`se_right_conventional`
`se_diff_conventional`
`t_stat_conventional`
`p_value_conventional`
`n`
`n_left`
`n_right`
`n_eff_left`
`n_eff_right`
`h_left`
`h_right`
`bandwidths`
`cutoff`
`p`
`q`
`fitselect`
`kernel`
`vcov_type`
`bwselect`
`bandwidth_method`
`masspoints`
`masspoints_adjusted`
`regularize`
`n_local_min`
`n_unique_min`
`report_all`

__init__(t_stat, p_value, f_left, f_right, f_diff, se_left, se_right, se_diff, f_left_conventional, f_right_conventional, f_diff_conventional, se_left_conventional, se_right_conventional, se_diff_conventional, t_stat_conventional, p_value_conventional, n, n_left, n_right, n_eff_left, n_eff_right, h_left, h_right, bandwidths, cutoff, p, q, fitselect, kernel, vcov_type, bwselect, bandwidth_method, masspoints, masspoints_adjusted, regularize, n_local_min, n_unique_min, report_all)#

Parameters:

t_stat (float)
p_value (float)
f_left (float)
f_right (float)
f_diff (float)
se_left (float)
se_right (float)
se_diff (float)
f_left_conventional (float | None)
f_right_conventional (float | None)
f_diff_conventional (float | None)
se_left_conventional (float | None)
se_right_conventional (float | None)
se_diff_conventional (float | None)
t_stat_conventional (float | None)
p_value_conventional (float | None)
n (int)
n_left (int)
n_right (int)
n_eff_left (int)
n_eff_right (int)
h_left (float)
h_right (float)
bandwidths (DataFrame | None)
cutoff (float)
p (int)
q (int)
fitselect (str)
kernel (str)
vcov_type (str)
bwselect (str)
bandwidth_method (str)
masspoints (str)
masspoints_adjusted (bool)
regularize (bool)
n_local_min (int)
n_unique_min (int)
report_all (bool)

Return type:

None

classmethod __new__(*args, **kwargs)#