---
title: "Quasi-experimental methods: DiD, IV, RDD, and weighting"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quasi-experimental methods: DiD, IV, RDD, and weighting}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
set.seed(1)
```

rmorie's quasi-experimental stack is fully native: no `did`,
`fixest`, `did2s`, `didimputation`, `rdrobust`, `AER`, `WeightIt`, or
`CBPS` at runtime. Each estimator is cross-validated against its
reference package in `tests/` and the canonical replication suite
(`tests/testthat/test-quasiex-replication.R`) reproduces the
published estimates on the bundled real datasets (LaLonde, Basque
Country, Lee 2008 incumbency, CigarettesSW).

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

## Difference-in-differences with automatic staggered handling

`morie_did()` inspects the treatment-timing structure: with a single
adoption cohort it runs the native TWFE estimator; with staggered
adoption it warns that a naive TWFE would be contaminated by
forbidden comparisons (Goodman-Bacon 2021) and switches to the
Callaway-Sant'Anna group-time estimator.

```{r did}
df <- expand.grid(id = 1:60, t = 1:8)
df$g <- ifelse(df$id <= 20, 4L, ifelse(df$id <= 40, 6L, NA))
df$y <- rnorm(nrow(df), sd = 0.5) +
  ifelse(!is.na(df$g) & df$t >= df$g, 2, 0)
fit <- suppressWarnings(morie_did(df, "y", "id", "t", "g",
                                  n_bootstrap = 49L))
fit
```

The modern estimators are available directly when you want a specific
one: `morie_did_sun_abraham()` (interaction-weighted event study),
`morie_did_borusyak()` (imputation), and `morie_did_did2s()`
(Gardner two-stage).

```{r sunab}
es <- morie_did_sun_abraham(df, "y", "id", "t", "g",
                            leads = 2L, lags = 2L)
es
```

## Instrumental variables with a weak-instrument gate

`morie_iv_2sls()` refuses to report a 2SLS point estimate when the
first-stage F is below the Staiger-Stock threshold of 10 and returns
the identification-robust Anderson-Rubin confidence set instead.

```{r iv}
n <- 300
z <- rnorm(n); u <- rnorm(n)
d <- 1.2 * z + 0.5 * u + rnorm(n)
y <- 2 * d + u + rnorm(n)
morie_iv_2sls(data.frame(y, d, z), "y", "d", "z")
```

## Regression discontinuity with bundled diagnostics

`morie_rdd()` returns the bias-corrected estimate together with the
McCrary manipulation check and placebo-cutoff estimates -- the
robustness steps applied papers routinely forget.

```{r rdd}
x <- runif(600, -1, 1)
y <- 1 + 2 * (x >= 0) + x + rnorm(600, sd = 0.5)
morie_rdd(data.frame(y, x), "y", "x")
```

## Propensity-score weighting

The `morie_weight_*` family produces a `morie_weight` object the rest
of the package composes with: logistic scores, entropy balancing,
CBPS, overlap weights, stabilization, trimming, a native
SuperLearner stack, and balance diagnostics.

```{r weights}
dw <- data.frame(t = rbinom(300, 1, 0.4),
                 x1 = rnorm(300), x2 = rnorm(300))
w <- morie_weight_ps(dw, "t", c("x1", "x2"), estimand = "ATT")
w
head(morie_weight_diagnostic(w, dw, "t", c("x1", "x2")))
```

## One pipeline end to end

The composition test (`tests/testthat/test-composition-pipeline.R`)
chains DAG construction, backdoor identification, matching,
weighting, estimation, refutation, and the MRM publication table on
the bundled LaLonde data -- one class system across every module.