Empirical Safeguards & Advanced Inference
Rigorous policy evaluation demands more than baseline correlations. Truveun applies frontier causal inference, continuous dosage formulations, and exhaustive sensitivity tests. Below is an illustrative example of our 10-step safeguard framework drawn from a recent empirical evaluation.
Methodological & Statistical Safeguards
Ten approaches supporting the estimates, from study design through sensitivity and inference.
* Note: Specific metrics, panel counts, and estimation parameters below are representative examples from a recent study to illustrate our standard methodology.
Methodological Note • Illustrative Case Study Only: The figures, sample dimensions (e.g., 119,504 NIBRS panel cells, 4,268 agencies), dosage thresholds (≥0.10 ALPRs per sworn officer), and falsification tiers presented above are drawn from a recent public safety technology evaluation as an exemplar case study. Truveun customizes and pre-registers an equivalent, domain-specific safeguard battery for every empirical evaluation—including municipal finance, local taxation, and transit-oriented zoning.
Advancing Modern Econometric Methods
Recent breakthroughs in econometric theory show that classical regressions often yield biased or sign-reversed estimates under staggered rollout. Here is how Truveun engineers reproducibility and causal truth into every study.
Frontier Staggered DiD & ETWFE
Traditional Two-Way Fixed Effects (TWFE) with staggered policy rollout can assign negative weights to treatment effects, introducing severe bias. We employ Poisson Extended Two-Way Fixed Effects (ETWFE) to guarantee non-negative weighting and robust estimates under treatment effect heterogeneity.
Continuous Dosage Modeling
Civic policy adoption is rarely binary. Rather than relying on simplistic on/off indicator dummies, we measure empirical treatment intensity (such as technology units per sworn officer or zoned housing capacity) and perform exposure sweeps from 0.10 through 0.5+ to ensure cutoff invariance.
Rambachan–Roth Sensitivity Bounds
Satisfying pre-trend tests does not guarantee parallel trends in the post-treatment horizon. Following Rambachan & Roth (2023), we compute honest sensitivity bounds, proving how large post-period trend violations would have to be to nullify the estimated effect.
Randomization & Placebo Falsifications
To prove results are not artifacts of chance or sampling noise, we execute 2,000 permutation replicates across never-treated units inheriting real activation timing, alongside strict falsification tiers on unrelated crimes and non-target geographies.
Spatial Spillovers & Interference
Directly addresses Stable Unit Treatment Value Assumption (SUTVA) violations and boundary displacement. We estimate spatial decay buffers, boundary contiguity contrasts, and neighboring spillover rings to ensure localized policy gains represent true net additions rather than displacement across borders.
Composition Robustness & Invariance
Protects against Simpson's paradox and denominator-mix artifacts. We decompose aggregate rates into distinct subgroup, category, and cohort strata, verifying that observed policy shifts reflect true underlying behavioral impacts rather than changes in reporting composition.
Pre-Registration & Open Scientific Verification
Truveun locks study cohorts, hypothesis formulations, and empirical specification plans before inspecting outcome data to prevent researcher degrees of freedom. We invite university scholars, research economists, and public leaders to review our methodological protocols.