What this vignette covers

The MRM (Multilevel Reconciliation Methodology) framework is a coordinated set of ten causal estimators paired with a multi-source data layer for Canadian carceral, police, and oversight data. This vignette uses the provincial Offender Tracking Information System (OTIS; published by the Ontario Ministry of the Solicitor General) restrictive-confinement microdata as an example, applies the ten-estimator ensemble to a binary-treatment design on dataset a01, and shows how to read the resulting summary.

The mathematical foundations are developed in the companion paper (Ruhela 2026, The MRM Framework; see citation("morie") for the full bibentry).

Loading OTIS

OTIS is shipped with the package; the morie_load_dataset() loader hides the SQLite-backed indirection.

library(rmorie)
otis <- morie_load_dataset("otis-2025-a01")
str(otis)

The canonical a01 design

For dataset a01 the canonical formulation is T_high_ac (a binary treatment derived from administrative-classification flags) on Y_vm_count (a count of a specific in-confinement observation) with the standard demographic covariate set. This is the design choice that the per-row MRM modules implement.

# Full ten-estimator ensemble on the canonical a01 design:
result <- morie_estimate_ate(
  data       = otis,
  outcome    = "Y_vm_count",
  treatment  = "T_high_ac",
  covariates = c("age", "sex", "region", "fiscal_year")
)
print(result)

The returned object summarises the IPW (Hajek), AIPW (Robins–Rotnitzky–Zhao), g-computation, propensity-score-matching (1:1 NN and five-strata subclass), IRM-DML (Chernozhukov et al. 2018), PLR-DML, and SuperLearner-stacked AIPW estimates. Multi-SE comparison (pooled, cluster on fiscal year, cluster on individual ID, two-way) is reported alongside the IRM-DML primary.

Augmented IPW

result_aipw <- morie_estimate_aipw(
  data       = otis,
  outcome    = "Y_vm_count",
  treatment  = "T_high_ac",
  covariates = c("age", "sex", "region", "fiscal_year")
)
print(result_aipw)

Aggregate companion: incidence-rate ratios

For aggregate (year-level) outcomes the analog is a Poisson or negative-binomial GLM with cluster-robust standard errors. The MRM framework reports both the per-row individual-level estimate (above) and the aggregate IRR family in parallel; see the companion paper for the formal aggregate-IRR notation.

Mandela classification

A separate Mandela-Rules classifier (UN Mandela Rules 43 and 44) is applied at both the federal and provincial levels. The provincial implementation uses a duration-only proxy that is documented explicitly in the framework paper. Federal counterpart analyses (SIU IAP, Sprott–Doob–Iftene) live in the companion Python module morie.tps_csi and morie.siu_iap.

Where to go next

The full MRM framework paper, including all ten estimators, multi-SE comparison, propensity calibration, and the Sprott–Doob–Iftene replication tables, is in the companion publication set (see citation("morie")).
The MORIE package paper covers the broader toolkit; see citation("morie") for the bibentry.
Citation: see citation("morie").