Causal evidence & experiments
Establish that a change caused an outcome, rather than that the two moved together.
29 of 388 tools.
Audit causal claim negative controls
Gate a causal effect claim using prespecified negative outcome/exposure controls, Benjamini-Hochberg multiplicity control, and an omnibus chi-square falsification test.
Audit cluster randomization integrity
Audit cluster-randomized experiments for practical baseline imbalance and differential outcome observation, with cluster-size-weighted standardized differences and assignment permutation diagnostics.
Audit growth incrementality experiment integrity
Audit aggregate randomized growth experiments before anyone trusts channel incrementality: enforce unique experimental units, nondegenerate logged propensities, both arms, control-spend discipline, baseline balance, spillover and evidence gates; then estimate propensity-weighted baseline-adjusted contribution, cluster-unit bootstrap uncertainty and incremental return on spend.
Audit joint outcome network integrity
Audit whether a company-specific Bayesian joint-outcome network is fit for reliance by validating point-in-time lineage, DAG and CPT completeness, effective support, protected-attribute exclusions, and strictly out-of-time outcome calibration against a baseline.
Audit staggered rollout identification
Audit staggered team-by-team adoption with not-yet-treated controls and require every simultaneous pre-period interval to fit inside a governed equivalence margin.
Design balanced stepped wedge rollout
Assign teams or other aggregate clusters to capacity-constrained rollout waves with pair-exchange optimization of cumulative causal balance, represented population, and rollout risk.
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 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 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 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 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 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 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.
Evaluate offline policy doubly robust
Estimate a proposed contextual policy's value from logged decisions using cross-fitted outcome models, doubly robust scores, paired bootstrap safety bounds, and overlap diagnostics.
Fit honest intervention policy tree
Learn an interpretable heterogeneous intervention rule using separate structure, effect-estimation, and untouched policy-evaluation samples.
Forecast growth channel response saturation
Learn organization-specific channel saturation from resolved aggregate incrementality estimates: fit a likelihood-weighted Bayesian grid of Hill response curves, reserve the newest periods for honest validation against a linear baseline, expose posterior boundary misspecification and evidence failures, and return contribution and marginal-return distributions for proposed spend levels.
Forecast joint engineering outcome distribution
Learn a company-local partially pooled discrete Bayesian network from complete mature observations, validate it strictly out of time against an independent baseline, and answer coherent conditional joint engineering-outcome queries with exact inference and Dirichlet posterior intervals.
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.
Monitor sequential intervention experiment
Monitor cumulative binary intervention outcomes with beta-binomial posteriors, Bayes factors, expected regret, and preregistered success, harm, or futility stopping.
Optimize correlated experiment sequence
Sequence pure-learning experiments over correlated intervention effects using conjugate Gaussian updates, Gauss-Hermite lookahead, early stopping, and terminal deployment value.
Optimize model averaged joint outcome decision
Choose a governed aggregate engineering action across competing plausible Bayesian-network structures using pseudo-Bayesian out-of-time model weights, coherent joint outcome worlds, causal-identification mass, weighted CVaR, worst-model regret, decision stability and the expected value of resolving model uncertainty.
Optimize multi period growth budget saturation
Allocate aggregate growth capital across channels and periods on coherent common scenarios while preserving channel-specific Hill saturation and carryover state: search discrete spend schedules, propagate contribution and unrestricted cash, and maximize expected net incremental value minus CVaR shortfall subject to total/period budgets, liquidity and contribution-probability gates, with exact certification or disclosed deterministic beam search.
Reconcile plan actual variance drivers
Reconcile plan-to-actual value variance with an exact, order-independent Shapley decomposition of a declared multilinear operating model.
Simulate contextual thompson bandit
Simulate Bayesian contextual Thompson sampling and quantify intervention reward, regret, and policy uncertainty.
Solve budgeted bayesian experiment portfolio
Choose a budget- and resource-feasible portfolio of Bayesian experiments whose correlated observations can change multiple governed deployment decisions.
Stress test causal effect robustness
Quantify the omitted-confounder partial-R² strength required to erase a causal point estimate or its statistical significance, benchmarked against observed covariates.