Point-treatment survival policies#

This study validates longitudinal TMLE on the held survival design under a policy at the one treatment decision. A known stochastic policy or a modified treatment policy sets the treatment at node 1, and identity nodes follow it. That is Díaz, Williams, Hoffman and Schenck (2023), Definition 1, with \(d_t(a_t, h_t) = a_t\) for \(t \ge 2\). The section one baseline treatment held over the nodes states the estimator and its exact-law evidence.

The study pairs each fit with R lmtp 1.5.4. In lmtp the held policy is trt of length K. That is K copied treatment columns. The first holds the policy value, and the others hold the observed value. With trt of length one, lmtp applies the policy once at every node. For a policy that is not idempotent, that is a different estimand.

What was compared#

setting	`cleverly`	R `lmtp`
datasets	1,600 samples of 2,000 rows for each scenario	the identical rows, with K copied treatment columns
scenarios	`policy`: the five-visit binary law, with \(q(1 \mid W) = 0.5 + 0.25 W_1 + 0.25 \cdot 1\{W_2 \ge 2\}\) against the natural course. `mtp`: the dose law on `0..5`, with `lmtp`’s Example 2.1 policy `a - 1` where `a - 1 >= 1`, against the natural course	the same
reported parameters	both risks and their difference at visits 1, 3 and 5	the same
mechanism	the law’s own treatment and retention probabilities	the density ratio from the same probabilities, written beside the replicate data. `lmtp` fits no ratio
sequential regressions	a quasibinomial regression on `W1`, `W2` and the treatment	`SL.glm` on the same design, with the dose as a factor
targeting	the intercept of each node, with the clever covariate in the loss weight	the intercept fluctuation weighted by the ratio
intervals	pointwise 95% Wald from the influence curve	the same, from the `lmtp` influence curve

The two fluctuations differ by construction, so the paired rows read under the default margins. The initial_estimate column is one quantity on the two sides: the node-1 regression at the policy treatment, fitted on pseudo-outcomes that the later nodes already targeted. No verdict reads it.

Replication 1,003 of mtp has two sequential regressions with quasi-complete separation. The quasibinomial learner keeps the last iterate with a warning, as R’s glm does.

Accuracy against known truth#

law	estimand	what was tested	implementation	bias (99% interval)	coverage	SE ratio	result
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=1]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000138 to 0.000554	0.9450	0.9983	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=1]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	R `lmtp`	-0.000138 to 0.000554	0.9450	0.9983	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=3]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000222 to 0.000846	0.9419	0.9873	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=3]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	R `lmtp`	-0.000224 to 0.000842	0.9425	0.9880	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=5]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000319 to 0.000850	0.9519	1.0085	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=5]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	R `lmtp`	-0.000319 to 0.000850	0.9519	1.0087	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=1]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000360 to 0.000752	0.9544	1.0419	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=1]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 1	R `lmtp`	-0.000360 to 0.000752	0.9544	1.0419	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=3]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000734 to 0.000973	0.9581	1.0309	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=3]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 3	R `lmtp`	-0.000737 to 0.000969	0.9575	1.0311	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=5]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000325 to 0.0016	0.9469	0.9937	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=5]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 5	R `lmtp`	-0.000322 to 0.0016	0.9469	0.9938	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000433 to 0.000409	0.9600	1.0320	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	R `lmtp`	-0.000433 to 0.000409	0.9600	1.0320	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000858 to 0.000471	0.9556	1.0239	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	R `lmtp`	-0.000858 to 0.000471	0.9556	1.0238	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000410 to 0.0012	0.9400	0.9725	pass
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	R `lmtp`	-0.000407 to 0.0012	0.9400	0.9725	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=1]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000287 to 0.000267	0.9425	0.9623	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=1]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	R `lmtp`	-0.000289 to 0.000266	0.9419	0.9610	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=3]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000605 to 0.000195	0.9519	1.0070	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=3]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	R `lmtp`	-0.000615 to 0.000186	0.9525	1.0066	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=5]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000497 to 0.000385	0.9556	1.0092	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=5]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	R `lmtp`	-0.000501 to 0.000381	0.9563	1.0089	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=1]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000261 to 0.000729	0.9400	0.9794	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=1]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 1	R `lmtp`	-0.000263 to 0.000728	0.9400	0.9792	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=3]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000513 to 0.0010	0.9531	1.0145	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=3]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 3	R `lmtp`	-0.000522 to 0.000995	0.9525	1.0149	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=5]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000371 to 0.0014	0.9575	1.0360	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=5]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 5	R `lmtp`	-0.000374 to 0.0014	0.9563	1.0358	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000210 to 0.000697	0.9481	0.9965	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	R `lmtp`	-0.000210 to 0.000697	0.9481	0.9965	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000229 to 0.0011	0.9531	1.0225	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	R `lmtp`	-0.000229 to 0.0011	0.9537	1.0224	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	`cleverly` LTMLE with a policy at a held baseline treatment	-0.000203 to 0.0013	0.9619	1.0366	pass
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	R `lmtp`	-0.000202 to 0.0013	0.9625	1.0366	pass

Agreement with the canonical implementation#

Comparator-backed: a paired comparison with lmtp on the same samples. Each row reports estimate equivalence within the declared margin, the RMSE ratio and the coverage difference, and names its comparison kind.

law	estimand	what was compared	paired difference	share of margin used	RMSE ratio bound	coverage difference	comparison kind	calibration resolution	result
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=1]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	3.518e-10	4.374e-07	1.0000	0	different estimator	8.393e-09 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=3]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	0.000003	0.0024	1.0018	-0.000625	different estimator	0.000948 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`ate_regimen[baseline minus one vs natural course @ t=5]`	difference in cumulative risk between the plans “lower the baseline dose by one wherever it stays at least one, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	-1.421e-07	0.000104	1.0007	0	different estimator	0.000715 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=1]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 1	-1.660e-10	1.283e-07	1.0000	0	different estimator	3.806e-09 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=3]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 3	0.000003	0.0017	1.0009	0.000625	different estimator	0.000757 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[baseline minus one @ t=5]`	cumulative risk under the plan lower the baseline dose by one wherever it stays at least one, then hold it at horizon t = 5	-0.000003	0.0012	1.0004	0	different estimator	0.000275 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	-5.178e-10	5.286e-07	1.0000	0	different estimator	7.155e-09 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	3.022e-07	0.000195	1.0001	0	different estimator	0.000191 vs 0.0500	equivalent
five visits, a held dose on 0 to 5, lmtp’s minus-one policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	-0.000003	0.0013	1.0002	0	different estimator	0.000224 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=1]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 1	0.000002	0.0024	1.0002	0.000625	different estimator	0.0023 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=3]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 3	0.000009	0.0097	1.0008	-0.000625	different estimator	0.0019 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`ate_regimen[baseline policy vs natural course @ t=5]`	difference in cumulative risk between the plans “draw the baseline arm from a known policy, then hold it” against “leave the observed treatment mechanism unchanged” at horizon t = 5	0.000004	0.0037	1.0011	-0.000625	different estimator	0.0015 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=1]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 1	0.000002	0.0013	1.0003	0	different estimator	0.000695 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=3]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 3	0.000009	0.0052	1.0013	0.000625	different estimator	0.0015 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[baseline policy @ t=5]`	cumulative risk under the plan draw the baseline arm from a known policy, then hold it at horizon t = 5	0.000002	0.0012	1.0005	0.0012	different estimator	0.000887 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=1]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 1	-5.574e-10	5.281e-07	1.0000	0	different estimator	3.734e-09 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=3]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 3	1.947e-07	0.000123	1.0001	-0.000625	different estimator	0.000212 vs 0.0500	equivalent
five visits, a held binary treatment, a known stochastic policy at baseline	`risk_regimen[natural course @ t=5]`	cumulative risk under the plan leave the observed treatment mechanism unchanged at horizon t = 5	-0.000001	0.000813	1.0002	-0.000625	different estimator	0.000258 vs 0.0500	equivalent

Theory properties#

property	cell	role	what was tested	what must hold	measured	result
`interval_calibration`	`mtp_t5__correctly_specified`	positive	baseline minus one against the natural course at visit 5: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9406 to 0.9678, SE ratio 0.9810 to 1.0681, empirical efficiency ratio 0.9732 to 1.0603, reported efficiency ratio 1.0378 to 1.0431	pass
`interval_calibration`	`mtp_t5__noise_control`	control	baseline minus one against the natural course at visit 5: one efficiency-bound unit of independent noise is added to each estimate	the empirical efficiency ratio must rise above the band	coverage 0.8177 to 0.8652, SE ratio 0.6959 to 0.7635, empirical efficiency ratio 1.3630 to 1.4952, reported efficiency ratio 1.0378 to 1.0430	pass
`interval_calibration`	`mtp_t5__shrunken_se_control`	control	baseline minus one against the natural course at visit 5: the reported standard errors are multiplied by a declared factor below one	the SE-ratio interval must fall below the calibration band	coverage 0.8072 to 0.8558, SE ratio 0.6860 to 0.7473, empirical efficiency ratio 0.9743 to 1.0614, reported efficiency ratio 0.7264 to 0.7300	pass
`interval_calibration`	`policy_t5__correctly_specified`	positive	baseline policy against the natural course at visit 5: both nuisances are correctly specified with an independently computed efficiency bound	SE ratio and coverage intervals both inside their calibration bands, with both efficiency-ratio intervals inside their bands	coverage 0.9328 to 0.9619, SE ratio 0.9415 to 1.0350, empirical efficiency ratio 0.9775 to 1.0742, reported efficiency ratio 1.0094 to 1.0143	pass
`interval_calibration`	`policy_t5__noise_control`	control	baseline policy against the natural course at visit 5: one efficiency-bound unit of independent noise is added to each estimate	the empirical efficiency ratio must rise above the band	coverage 0.8171 to 0.8646, SE ratio 0.6737 to 0.7394, empirical efficiency ratio 1.3689 to 1.5018, reported efficiency ratio 1.0094 to 1.0143	pass
`interval_calibration`	`policy_t5__shrunken_se_control`	control	baseline policy against the natural course at visit 5: the reported standard errors are multiplied by a declared factor below one	the SE-ratio interval must fall below the calibration band	coverage 0.8006 to 0.8500, SE ratio 0.6595 to 0.7230, empirical efficiency ratio 0.9793 to 1.0740, reported efficiency ratio 0.7065 to 0.7099	pass

The positive cells fit the saturated cell means for every nuisance, so the mechanism and the ratio numerator are estimated. Each efficiency bound is the exact standard deviation of the efficient influence function over the law’s support. For the natural course and the modified treatment policy, the treatment mechanism is part of the parameter, and the bound carries its term.

Measured values#

Names beginning margin: are thresholds declared before the run. Everything else is measured from the committed results and checked at the precision printed.

quantity	value	source
`replicates`	1600	primary replications per scenario
`n`	2000	observations per primary replication
`independent_tests_total`	36	implementation-estimand tests against the truth
`independent_tests_passed`	36	of those, passing
`paired_tests_total`	18	paired comparisons with `lmtp`
`paired_tests_passed`	18	of those, passing
`property_cells_total`	6	repeated-sampling property cells
`property_cells_passed`	6	cells whose own and family verdicts pass
`max_standardized_bias`	0.0472	largest absolute primary bias in empirical standard deviations
`min_coverage`	0.9400	lowest measured primary-study coverage
`min_se_ratio_ci_lower`	0.9202	lowest bootstrap primary SE-ratio endpoint
`max_se_ratio_ci_upper`	1.0941	highest bootstrap primary SE-ratio endpoint
`margin:alpha`	0.0500	nominal size of the reported intervals, and the family level of the rule
`margin:bootstrap_replicates`	10000	resamples behind every bootstrap interval
`margin:calibration_coverage_lower`	0.9200	calibration-cell coverage band, lower limit. No cell of this study reads it
`margin:calibration_coverage_upper`	0.9800	calibration-cell coverage band, upper limit. No cell of this study reads it
`margin:calibration_noninferiority`	0.0500	largest external-comparison calibration excess bound
`margin:calibration_se_ratio_lower`	0.9300	calibration-cell SE-ratio band, lower limit. No cell of this study reads it
`margin:calibration_se_ratio_upper`	1.0700	calibration-cell SE-ratio band, upper limit. No cell of this study reads it
`margin:confidence_level`	0.9900	confidence level of every Monte Carlo interval
`margin:coverage_floor`	0.9000	validity floor the coverage lower endpoint must clear
`margin:coverage_noninferiority`	-0.0250	smallest external-comparison coverage difference bound
`margin:efficiency_ratio_lower`	0.9000	efficiency-ratio lower bound
`margin:efficiency_ratio_upper`	1.1000	efficiency-ratio upper bound
`margin:excluded_slope`	-0.2500	slower rate a root-n interval must exclude
`margin:minimum_power`	0.8000	rejection lower bound a power cell must clear
`margin:nominal_coverage`	0.9500	nominal coverage those intervals claim
`margin:over_coverage_ceiling`	0.9900	above this, coverage is conservative rather than invalid
`margin:paired_difference`	0.1500	paired similarity margin, in pooled empirical standard deviations
`margin:rmse_noninferiority`	1.1000	largest external-comparison RMSE ratio bound
`margin:root_n_slope`	-0.5000	contraction rate root-n asymptotics predict
`margin:root_n_slope_lower`	-0.6250	accepted root-n slope band, lower limit
`margin:root_n_slope_upper`	-0.3750	accepted root-n slope band, upper limit
`margin:se_ratio_sanity_lower`	0.8000	SE-ratio screen, lower limit
`margin:se_ratio_sanity_upper`	1.2000	SE-ratio screen, upper limit
`margin:shrunken_se_factor`	0.7000	negative-control SE multiplier
`margin:standardized_bias`	0.2500	bias equivalence margin, in empirical standard deviations
`margin:type_i_ceiling`	0.1000	the rate a positive cell must bound and a control must exceed

Limitations#

limit	what it means for use
a policy at node 1 only	a plan that changes the treatment after baseline is refused on a held design
the mechanism is the law’s own in the paired fits	both sides read the same probabilities, so the pairs test the targeting and the recursion, not the mechanism fit
the paired rows are not an exactness check	the two fluctuations differ by construction
one categorical dose law	a continuous baseline dose has fast-tier evidence only
calibration only	the property cells measure interval calibration. Double robustness and rates for the held design are in point-treatment survival

Reproduction#

The manifest records the seeds, the margins, the estimator configuration, the source hashes and the result hashes. Run python -m tests.canonical.point_survival_policies.regenerate to regenerate the artifacts. The replications and the property results carry every published row.