Tools that estimate
Measure an effect or quantity from the evidence you have.
54 of 388 tools.
Attribute commercial dependency tail loss
Calculate expected loss, VaR and CVaR for commercial value concentrated in shared technical components, then allocate every modeled tail-loss dollar exactly once across components with normalized negative-log survival hazard rather than overlapping leave-one-out sensitivities.
Cluster process markov archetypes
Discover privacy-eligible workflow archetypes from aggregate Markov transition counts using empirical-Bayes shrinkage, Jensen-Shannon k-medoids, silhouette quality, and posterior assignment stability.
Discover environment invariant predictive model
Search every nonempty subset of up to eight candidate features for a sparse predictive relationship whose validation residual bias and error remain within governed limits across represented environments, select without touching the test split, and compare the chosen model once against the full model on future-held-out environment data.
Estimate budget contingency reserve
Size an engineering or investment contingency reserve from one coherent joint cost distribution, stress represented scenario probabilities inside a governed total-variation radius, preserve natural offsets, and exactly reconcile robust tail overrun to aggregate cost items.
Estimate cannibalization adjusted feature value
Estimate feature value after posterior cannibalization of legacy contribution, using aligned adoption scenarios, beta-binomial substitution uncertainty, and value plus substitution-risk gates.
Estimate causal value of execution fidelity
Estimate how much outcome value an additional unit of implementation fidelity causes by using randomized enablement as an encouragement instrument, with whole-cluster arm bootstrap, first-stage, balance, negative-control and ratio-stability gates.
Estimate competing delivery risks
Estimate age-conditional probabilities of delivery, cancellation, escalation, or remaining active with Aalen-Johansen competing risks and bootstrap intervals.
Estimate coordination network percolation
Estimate organizational network tipping points under random versus targeted aggregate-unit loss, with weighted connected-component curves, Monte Carlo intervals, and structural-hub diagnostics.
Estimate cost of delay distribution
Translate probabilistic delivery delay into discounted contribution-value loss, permanent value decay, and governed penalties, including expected cost, tail cost, and the probability of material exposure.
Estimate decision reversal probability
Estimate how often planned evidence would reverse the current decision under a correlated Bayesian preposterior model, while separating fragility, regret, and net information value.
Estimate delivery delay value at risk
Translate aligned portfolio completion-date draws into expected delay loss, VaR/CVaR, dependence amplification, and initiative tail attribution.
Estimate dynamic execution factor
Extract a direction-aligned latent execution factor from aggregate metric vectors and forecast its level and velocity with a likelihood-tuned local-linear-trend state-space model.
Estimate engineering extreme value risk
Estimate rare incident, delay, loss, or pipeline-duration return levels with peaks-over-threshold generalized-Pareto fitting, bootstrap uncertainty, and threshold-stability diagnostics.
Estimate engineering learning curve
Estimate a team-fixed-effects power-law learning curve with work-size adjustment, cluster bootstrap uncertainty, and a defect-rate quality guardrail.
Estimate engineering portfolio VAR
Estimate correlated cost, schedule, success, value-decay and portfolio downside VaR/CVaR with initiative tail attribution.
Estimate estimate at completion distribution
Turn bottom-up component actuals and locally calibrated remaining-cost p50/p90 estimates into a correlated Gaussian-copula lognormal estimate-at-completion distribution with antithetic simulation, budget-breach probability, CVaR, correlation uplift, finite-draw error, and exactly reconciled component tail contributions.
Estimate feature incremental value
Estimate rollout value from segment-level treated/control outcomes with beta-binomial uplift posteriors, finance-owned contribution economics, and a probability-of-positive-value gate.
Estimate financing dilution scenarios
Estimate financing dilution with a scenario cap-table waterfall that solves pre-money option-pool top-ups and capped or discounted convertible claims before allocating post-money ownership.
Estimate FX exposure for engineering
Measure base-currency engineering cash-flow exposure across coherent amount and FX-rate scenarios, preserving natural netting, executable hedge payoffs and premiums, expected loss, CVaR, hedge effectiveness, and exactly reconciled currency tail contributions.
Estimate lee bounds under attrition
Partially identify a randomized treatment effect under differential outcome attrition using direction-aware fractional Lee trimming and bootstrap outer bounds.
Estimate liquidity at risk
Estimate liquidity-at-risk, tail funding need, committed-facility exhaustion probability, and residual unfunded shortfall from aligned operating paths.
Estimate longitudinal policy effect MSM
Estimate repeated-intervention regime effects with stabilized inverse-probability weights, an explicit marginal structural model, cluster bootstrap uncertainty, and positivity gates.
Estimate marginal engineering ROI
Evaluate an ordered engineering investment curve increment by increment, stopping at the first increment that misses its marginal ROI or downside-probability hurdle.
Estimate model risk reserve
Calculate an explicit model-risk reserve from the upper weighted quantile of competing approved models' CVaR loss relative to their weighted CVaR, with disagreement and model-level diagnostics.
Estimate multilevel metric generalizability
Decompose aggregate management-metric variance into unit, period, and residual components, bootstrap reliability, and calculate the sampling needed for dependable comparisons.
Estimate network direct and spillover effects
Estimate direct, neighbor-spillover, and total effects under Bernoulli-randomized network interference using exact exposure probabilities and randomization inference.
Estimate platform network option value
Value when to activate a shared platform under endogenous network adoption with an exact finite-horizon Markov dynamic program; optimize the invest/wait policy by observed adopter state, compare it with every fixed launch date and never investing, and reconcile option value, investment timing, and adoption quantiles.
Estimate portfolio company execution beta
Estimate company sensitivity to an external portfolio execution factor using company regressions, random-effects heterogeneity, empirical-Bayes shrinkage, uncertainty intervals, and systematic variance shares.
Estimate portfolio diversification benefit
Measure coherent portfolio diversification by comparing joint-scenario CVaR with standalone CVaRs and reconciling Euler tail-risk contributions, stress loss, and concentration gates.
Estimate pricing experiment value
Choose pricing experiment arms by posterior future contribution, conversion-harm probability, and a model-conditional perfect-information value upper bound.
Estimate productivity rebound effect
Estimate how much aggregate capacity released by a productivity intervention is absorbed by induced output or workload using stacked matched-cohort log difference-in-differences; separate fixed-output efficiency, induced output and total resource use, audit pretrends, reconcile the log identity, and cluster-bootstrap rebound uncertainty including backfire above 100 percent.
Estimate randomized causal mediation
Decompose a randomized intervention into natural direct and mediated effects with optional treatment-mediator interaction, bootstrap intervals, and total-effect reconciliation.
Estimate real option abandonment boundary
Learn a continuous-state project abandonment policy with cross-fitted least-squares Monte Carlo, explicit salvage economics, option uplift precision, support warnings, and boundary-shape diagnostics.
Estimate risk contribution shapley
Allocate portfolio CVaR loss across initiatives, companies, services, or risk factors with exact subset Shapley values or disclosed sampled permutations while preserving diversification and hedge contributions.
Estimate role adjusted contribution
Estimate role-relative outcome contributions with empirical-Bayes shrinkage, uncertainty, provenance, and cohort privacy.
Estimate software reliability growth
Estimate long-run software reliability growth with a power-law nonhomogeneous Poisson process, bootstrap trend evidence, and future incident exposure.
Estimate staggered policy rollout effects
Estimate cohort-aware dynamic effects of a team-by-team policy rollout against not-yet-treated controls, with a simultaneous pretrend identification gate.
Estimate switchback policy effect
Estimate randomized operational switchback effects with unit and period fixed effects, declared washout exclusions, distributed carryover lags, overlap enforcement, and whole-unit bootstrap uncertainty.
Estimate synthetic control impact
Estimate intervention effects against a constrained donor-weighted counterfactual with placebo inference and donor sensitivity.
Estimate systemic portfolio contagion
Estimate nonlinear financial distress propagation across a directed portfolio network under coherent joint shocks, separating direct from contagion loss and reporting CVaR, convergence, spectral instability, tail attribution, and finite-round loss influence.
Estimate team stochastic frontier
Estimate a Cobb-Douglas team production frontier with half-normal inefficiency, symmetric noise, conditional efficiency, and bootstrap uncertainty.
Estimate threshold policy effect rdd
Estimate a local sharp or fuzzy regression-discontinuity effect for threshold-assigned policies, with weak-first-stage, density-manipulation, placebo, and bootstrap diagnostics.
Estimate transportable intervention effect
Transport intervention effects to a target environment with similarity-weighted random-effects meta-regression, support diagnostics, and leave-one-environment-out validation.
Estimate transportable root cause probability
Estimate how likely a mechanism actually caused an observed failure using transport-weighted Bayesian random-effects MCMC across remediation studies, posterior probability of necessity, convergence diagnostics and mandatory unmeasured-confounding sensitivity.
Estimate value of flexibility
Value a strategy's pre-action signal-contingent flexibility against its best fixed action, including enablement cost, tail loss, and perfect-information headroom.
Fit anchor regression shift robust model
Fit anchor regression across declared operating environments, penalizing residual variation predictable from environment anchors over a governed gamma path; choose robustness strength only on held-out worst-environment RMSE; and expose average fit, environment bias, coefficients, and leave-one-environment stability without claiming generic or causal invariance.
Fit cross fitted isotonic recalibrator
Repair monotone probability calibration with pool-adjacent-violators while using cross-fitting and a paired bootstrap to prove out-of-sample Brier improvement.
Fit honest intervention policy tree
Learn an interpretable heterogeneous intervention rule using separate structure, effect-estimation, and untouched policy-evaluation samples.
Fit incident hawkes process
Estimate incident aftershock dynamics with a stationary exponential Hawkes process and conditionally simulate near-term incident counts.
Fit team behavior regime HMM
Learn persistent privacy-safe team operating regimes and transitions with a Gaussian hidden Markov model.
Infer competing root cause posterior
Rank competing, compound and unknown root mechanisms from company-local resolved incidents using partially pooled Dirichlet-Beta learning, strict temporal holdout scoring, reliability-tempered signals and posterior uncertainty rather than a single brittle traceback winner.
Infer revealed policy preferences maxent irl
Infer aggregate linear state-feature rewards and their implied stochastic policy from sequential demonstrations using finite-horizon maximum-causal-entropy inverse reinforcement learning.
Infer stability selected temporal metric graph
Infer a compact aggregate temporal dependency graph with a chronologically held-out ridge VAR, moving-block coefficient bootstrap, practical-effect stability selection, and false-discovery control.
Measure decision policy realized value
Measure candidate-versus-baseline realized net value from logged decisions with cross-fitted doubly robust policy scores, full action propensities, cluster bootstrap, importance-weight clipping, positivity mass, effective sample size, logging-policy calibration and cumulative value—so Gitrevio can substantiate decision ROI without relabeling correlation as impact.