---
title: "The native engines: DAG to publication table without leaving rmorie"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{The native engines: DAG to publication table without leaving rmorie}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
set.seed(2026)
```

rmorie implements every statistical algorithm it exposes natively —
this vignette walks the whole pipeline on one simulated study using
only `library(rmorie)`. No MatchIt, no survey, no DoubleML, no
dagitty, no fixest: each step below runs on an engine that lives in
this package and is cross-validated against the reference
implementation in `tests/cross/`.

```{r}
library(rmorie)
```

## The data

A simulated observational study: three confounders drive both a
binary treatment and a continuous outcome, with a true effect of 0.9.

```{r}
n <- 600
age  <- rnorm(n); educ <- rnorm(n); re74 <- rnorm(n)
treat <- rbinom(n, 1, plogis(0.4 * age + 0.3 * educ + 0.3 * re74))
re78  <- 1 + 0.9 * treat + 0.5 * age + 0.4 * educ + 0.6 * re74 +
  rnorm(n)
d <- data.frame(age, educ, re74, treat, re78)
```

## 0. Guard the categories first (module 25)

Before any model: audit the categorical columns. Silent
category-mapping errors — numeric codes imported without their
labels, positional recodes, alphabetical releveling — have corrupted
published disparity analyses for years at a time. rmorie makes them
structurally impossible:

```{r}
grp_codes <- sample(c("1", "2"), n, replace = TRUE)   # an import hazard
morie_audit_categories(data.frame(group = factor(grp_codes)))

grp <- morie_safe_recode(grp_codes, c("1" = "Control", "2" = "Program"))
morie_crosstab_verify(grp_codes, grp,
                      c("1" = "Control", "2" = "Program"))
```

## 1. The DAG (module 13)

```{r}
dag <- morie_dag(
  edges = c("re74 -> treat", "re74 -> re78",
            "age -> treat",  "age -> re78",
            "educ -> treat", "educ -> re78",
            "treat -> re78"),
  exposure = "treat", outcome = "re78")
dag
```

## 2. Identification

Backdoor identification runs Bayes-Ball d-separation with the
canonical adjustment set (validated against dagitty in
`tests/cross/`):

```{r}
id <- morie_dag_identify(dag)
id$identified
id$adjustment_set
```

## 3. Matching on the identified set (module 1)

```{r}
m <- morie_matching_nearest_neighbor(d, "treat", id$adjustment_set)
nrow(m$match_pairs)
morie_matching_balance(m$matched_data, "treat",
                       id$adjustment_set)$max_smd
```

## 4. Estimation through the DAG (modules 10 + 13)

`morie_dag_estimate()` routes the identified set into the native
estimators — here double machine learning (cross-fit ridge + logit
nuisances, Neyman-orthogonal score):

```{r}
ate <- morie_dag_estimate(dag, d, method = "backdoor.dml")
est <- if (!is.null(ate$estimate)) ate$estimate else ate$ate
c(estimate = est)
```

## 5. Refutation (module 13)

DoWhy-style robustness checks, all native — a placebo treatment
should erase the effect; subsets and a random common cause should
leave it stable:

```{r}
for (mth in c("placebo_treatment", "random_common_cause",
              "data_subset")) {
  rf <- morie_dag_refute(dag, d, mth, n_reps = 10L)
  cat(sprintf("%-22s passed: %s\n", mth, rf$passed))
}
```

## 6. The MRM pipeline and a publication table (module 24)

The MRM flagship composes several native estimators on the same
specification, applies a multiple-testing correction across them,
pools an inverse-variance consensus, and renders the table with
rmorie's own renderer (text, markdown, LaTeX, or HTML):

```{r}
eff <- morie_mrm_estimate_causal_effect(
  d, "treat", "re78", id$adjustment_set,
  methods = c("matching", "ate", "dml"))
cat(morie_mrm_report(eff, format = "markdown"), sep = "\n")
```

## Where to look next

* Every engine's cross-validation lives in `tests/cross/` (one file
  per reference package) and its speed in `inst/benchmarks/`.
* The quasi-experimental family (DiD, event studies, synthetic
  control, RDD, ITS, IV) follows the same pattern — see
  `?morie_did_group_time_att`, `?morie_synth_control`,
  `?morie_rdd_sharp`, `?morie_its`, `?morie_iv_tsls`.
* `morie_crypto_pqc_inventory()` reports the post-quantum coverage of
  your build.