---
title: "Matching Workflows"
author: "Gilles Colling"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Matching Workflows}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
library(couplr)
library(dplyr)
library(ggplot2)
```

## Overview

Matching is a fundamental technique for creating comparable groups in observational studies. Unlike random assignment, observational data often contain systematic differences between treatment and control groups that confound causal inference. Matching addresses this by pairing similar units based on observed characteristics, creating "apples-to-apples" comparisons.

This vignette follows a **complete workflow** from raw data to publication-ready results, using a realistic job training evaluation as the running example. You'll learn to handle the full pipeline: preprocessing, matching, assessment, and reporting.

### Who This Vignette Is For

**Audience**: Researchers, epidemiologists, economists, data scientists working with observational data

**Prerequisites**:

- Familiarity with basic `couplr` usage (`vignette("getting-started")`)

- Understanding of causal inference concepts (treatment effects, confounding)

- Basic statistics (means, standard deviations, t-tests)

**What You'll Learn**:

- How to match treatment and control units with `match_couples()`

- Automatic preprocessing: scaling, health checks, categorical encoding

- Assessing match quality with `balance_diagnostics()`

- When to use optimal vs greedy matching

- Creating publication-ready balance tables

**Time to complete**: 30-45 minutes

### Documentation Roadmap

| Vignette | Focus | Difficulty |
|----------|-------|------------|
| Getting Started | Basic LAP solving, API introduction | Beginner |
| Algorithms | Mathematical foundations, solver selection | Intermediate |
| **Matching Workflows** | Production matching pipelines | Intermediate |
| Pixel Morphing | Scientific applications, approximations | Advanced |

*You are here: Matching Workflows*

### The Running Example: Job Training Evaluation

Throughout this vignette, we evaluate a **job training program's effect on earnings**. The challenge: participants self-selected into the program, so they differ systematically from non-participants in age, education, and prior earnings. Without matching, any earnings difference could be due to these baseline differences rather than the program itself.

**Our goal**: Create comparable treatment and control groups, assess balance quality, and estimate the treatment effect credibly.

### Key Features

- Automatic preprocessing with health checks and smart scaling

- Optimal matching via linear assignment algorithms

- Fast greedy matching for large datasets (n > 5,000)

- Blocking and stratification support

- Comprehensive balance diagnostics

- Publication-ready output tables

## Problem Definition

### The Matching Problem

Given two groups of units:

- **Left group** (treatment, exposed, cases): $n_L$ units

- **Right group** (control, unexposed, controls): $n_R$ units

Each unit $i$ has a covariate vector $\mathbf{x}_i \in \mathbb{R}^p$ describing characteristics we want to balance (age, income, education, etc.).

**Goal:** Find one-to-one matches that minimize total distance:

$$
\min_{\pi} \sum_{i=1}^{n} d(\mathbf{x}_i^L, \mathbf{x}_{\pi(i)}^R)
$$

where $d(\cdot, \cdot)$ is a distance function (typically Euclidean or Mahalanobis) and $\pi$ is a matching assignment.

### Why Matching Matters

**Without matching:**

- Groups may differ systematically on important covariates

- Treatment effect estimates are confounded by these differences

- Statistical adjustments (regression) rely on untestable assumptions

**With matching:**

- Create comparable groups that differ primarily in treatment status

- Balance covariates to reduce confounding

- Improve causal inference by mimicking randomized experiments

- Transparent, non-parametric preprocessing step

### Distance Metrics

The quality of matching depends on the distance metric:

**Euclidean distance** (after scaling):
$$
d(\mathbf{x}_i, \mathbf{x}_j) = \sqrt{\sum_{k=1}^{p} (x_{ik} - x_{jk})^2}
$$

**Mahalanobis distance** (accounts for correlations):
$$
d(\mathbf{x}_i, \mathbf{x}_j) = \sqrt{(\mathbf{x}_i - \mathbf{x}_j)^\top \Sigma^{-1} (\mathbf{x}_i - \mathbf{x}_j)}
$$

**Propensity score distance** (single dimension):
$$
d(i, j) = |p_i - p_j|
$$

where $p_i = P(\text{treatment} \mid \mathbf{x}_i)$ is the propensity score.

`couplr` defaults to Euclidean distance with automatic scaling, which works well for most applications.

## Basic Usage

### Creating a Matched Sample

The simplest workflow uses `match_couples()` with automatic preprocessing:

```{r basic-example}
# Simulate observational data
set.seed(123)
n_left <- 100
n_right <- 150

# Treatment group (tends to be younger, higher income)
left_data <- tibble(
  id = 1:n_left,
  age = rnorm(n_left, mean = 45, sd = 10),
  income = rnorm(n_left, mean = 60000, sd = 15000),
  education = sample(c("HS", "BA", "MA"), n_left, replace = TRUE),
  group = "treatment"
)

# Control group (older, lower income on average)
right_data <- tibble(
  id = 1:n_right,
  age = rnorm(n_right, mean = 52, sd = 12),
  income = rnorm(n_right, mean = 50000, sd = 18000),
  education = sample(c("HS", "BA", "MA"), n_right, replace = TRUE),
  group = "control"
)

# Perform optimal matching
result <- match_couples(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  auto_scale = TRUE,
  return_diagnostics = TRUE
)

# View matched pairs
head(result$pairs)

# Summary statistics
result$info
```

The result contains:

- `pairs`: Matched pairs with IDs and distances

- `info`: Matching method, counts, total distance

- `left_unmatched`, `right_unmatched`: Units without matches

- `cost_matrix`: Full distance matrix (if `return_diagnostics = TRUE`)

### Understanding the Output

```{r inspect-output, fig.alt="Histogram showing the distribution of match distances, with most matches having low distances indicating good match quality"}
# How many matched?
cat("Matched pairs:", result$info$n_matched, "\n")
cat("Unmatched left:", nrow(result$left_unmatched), "\n")
cat("Unmatched right:", nrow(result$right_unmatched), "\n")

# Distribution of match distances
summary(result$pairs$distance)

# Visualize match quality
ggplot(result$pairs, aes(x = distance)) +
  geom_histogram(bins = 30, fill = "#29abe0", alpha = 0.7) +
  labs(
    title = "Distribution of Match Distances",
    x = "Euclidean Distance (scaled)",
    y = "Count"
  ) +
  theme_minimal() +
  theme(plot.background = element_rect(fill = "transparent", color = NA),
        panel.background = element_rect(fill = "transparent", color = NA),
        panel.grid = element_blank())
```

**Interpretation:**

- Lower distances indicate better matches (more similar units)

- Median distance provides typical match quality

- Large distances suggest poor matches (consider caliper constraints)

## Automatic Preprocessing

### Why Preprocessing Matters

Raw covariates often have:

- **Different scales**: Age in years (20-80), income in dollars (20,000-200,000)

- **Different units**: Continuous (age), categorical (education), binary (smoker)

- **Data quality issues**: Missing values, constant variables, extreme outliers

Without preprocessing, high-variance variables dominate distance calculations.

### Smart Scaling with `auto_scale = TRUE`

When `auto_scale = TRUE`, `match_couples()` automatically:

1. **Detects problematic variables**

   - Constant variables (SD = 0) -> excluded with warning

   - High missingness (>50%) -> warned

   - Extreme skewness (|skew| > 2) -> informed

2. **Applies appropriate scaling**

   - Continuous variables -> scaled by selected method

   - Binary variables (0/1) -> used as-is

   - Categorical -> converted to numeric if ordered

3. **Handles categorical variables**

   - Unordered factors -> converted to binary indicators

   - Ordered factors -> converted to numeric ranks

```{r preprocessing-demo}
# Create data with scaling challenges
set.seed(456)
challenging_data <- tibble(
  id = 1:50,
  age = rnorm(50, 50, 10),                    # Years (reasonable scale)
  income = rnorm(50, 60000, 20000),           # Dollars (large scale)
  bmi = rnorm(50, 25, 5),                     # Ratio (small scale)
  smoker = sample(0:1, 50, replace = TRUE),   # Binary
  education = factor(
    sample(c("HS", "BA", "MA", "PhD"), 50, replace = TRUE),
    ordered = TRUE,
    levels = c("HS", "BA", "MA", "PhD")
  )
)

left_chal <- challenging_data[1:25, ]
right_chal <- challenging_data[26:50, ]

# Match WITHOUT auto-scaling (income dominates)
result_no_scale <- match_couples(
  left_chal, right_chal,
  vars = c("age", "income", "bmi"),
  auto_scale = FALSE
)

# Match WITH auto-scaling (all variables contribute)
result_scaled <- match_couples(
  left_chal, right_chal,
  vars = c("age", "income", "bmi"),
  auto_scale = TRUE,
  scale = "robust"  # Median/MAD scaling (robust to outliers)
)

# Compare match quality
cat("Without scaling - mean distance:", mean(result_no_scale$pairs$distance), "\n")
cat("With scaling - mean distance:", mean(result_scaled$pairs$distance), "\n")
```

### Scaling Methods

Three scaling strategies available via `scale` parameter:

**1. Robust scaling** (`scale = "robust"`, default):
$$
x_{\text{scaled}} = \frac{x - \text{median}(x)}{\text{MAD}(x)}
$$

- Uses median and median absolute deviation (MAD)

- Resistant to outliers and skewness

- Best for: Real-world data with potential outliers

**2. Standardization** (`scale = "standardize"`):
$$
x_{\text{scaled}} = \frac{x - \text{mean}(x)}{\text{SD}(x)}
$$

- Classical z-score normalization

- Assumes approximate normality

- Best for: Clean, normally-distributed data

**3. Range scaling** (`scale = "range"`):
$$
x_{\text{scaled}} = \frac{x - \min(x)}{\max(x) - \min(x)}
$$

- Scales to [0, 1] range

- Preserves exact relationships

- Best for: Bounded variables, visualization

```{r scaling-comparison}
# Demonstrate scaling methods
demo_var <- c(10, 20, 25, 30, 100)  # Contains outlier (100)

# Function to show scaling
show_scaling <- function(x, method) {
  left_demo <- tibble(id = 1:3, var = x[1:3])
  right_demo <- tibble(id = 1:2, var = x[4:5])

  result <- match_couples(
    left_demo, right_demo,
    vars = "var",
    auto_scale = TRUE,
    scale = method,
    return_diagnostics = TRUE
  )

  # Extract scaled values from cost matrix
  cat(method, "scaling:\n")
  cat("  Original:", x, "\n")
  cat("  Distance matrix diagonal:", diag(result$cost_matrix)[1:2], "\n\n")
}

show_scaling(demo_var, "robust")
show_scaling(demo_var, "standardize")
show_scaling(demo_var, "range")
```

### Health Checks and Warnings

The preprocessing system provides informative diagnostics:

```{r health-checks, eval=FALSE}
# Create data with issues
problematic_data <- tibble(
  id = 1:100,
  age = rnorm(100, 50, 10),
  constant_var = 5,                           # No variation - will warn
  mostly_missing = c(rnorm(20, 50, 10), rep(NA, 80)),  # >50% missing
  extreme_skew = rexp(100, rate = 0.1)       # Very skewed
)

# Attempt matching - will show warnings
result <- match_couples(
  problematic_data[1:50, ],
  problematic_data[51:100, ],
  vars = c("age", "constant_var", "mostly_missing", "extreme_skew"),
  auto_scale = TRUE
)

# Warning messages will indicate:
# - "constant_var excluded (SD = 0)"
# - "mostly_missing has 80% missing values"
# - "extreme_skew has high skewness (3.2)"
```

## Optimal vs Greedy Matching

For large datasets (n > 5,000), optimal matching becomes computationally expensive. `couplr` provides fast greedy alternatives.

### When to Use Each Approach

**Optimal matching** (`method = "optimal"`):

- Minimizes total distance (globally optimal solution)

- Uses linear assignment algorithms ($O(n^3)$ complexity)

- Suitable for: n < 5,000, when optimality is critical

- Typical runtime: <1 second for n=1,000, ~10 seconds for n=3,000

**Greedy matching** (`method = "greedy"`):

- Fast approximation (10-100x faster)

- Three strategies: sorted, row_best, pq

- Suitable for: n > 5,000, exploratory analysis, when speed matters

- Typical runtime: <1 second for n=10,000

```{r optimal-vs-greedy}
# Create moderately large dataset
set.seed(789)
n <- 1000
large_left <- tibble(
  id = 1:n,
  x1 = rnorm(n),
  x2 = rnorm(n),
  x3 = rnorm(n)
)
large_right <- tibble(
  id = 1:n,
  x1 = rnorm(n),
  x2 = rnorm(n),
  x3 = rnorm(n)
)

# Optimal matching
time_optimal <- system.time({
  result_optimal <- match_couples(
    large_left, large_right,
    vars = c("x1", "x2", "x3"),
    method = "hungarian"
  )
})

# Greedy matching (row_best strategy)
time_greedy <- system.time({
  result_greedy <- greedy_couples(
    large_left, large_right,
    vars = c("x1", "x2", "x3"),
    strategy = "row_best"
  )
})

# Compare
cat("Optimal matching:\n")
cat("  Time:", round(time_optimal["elapsed"], 3), "seconds\n")
cat("  Mean distance:", round(mean(result_optimal$pairs$distance), 4), "\n\n")

cat("Greedy matching:\n")
cat("  Time:", round(time_greedy["elapsed"], 3), "seconds\n")
cat("  Mean distance:", round(mean(result_greedy$pairs$distance), 4), "\n")
cat("  Speedup:", round(time_optimal["elapsed"] / time_greedy["elapsed"], 1), "x\n")
```

### Greedy Strategies

Three greedy strategies available via `greedy_couples()`:

**1. Sorted** (`strategy = "sorted"`):

- Sort all possible pairs by distance

- Assign greedily from best to worst

- Best quality among greedy methods

- Slowest greedy option (still much faster than optimal)

```
Algorithm:
1. Compute all n_L × n_R distances

2. Sort pairs by distance (ascending)

3. For each pair in sorted order:

   - If both units unmatched, assign them

4. Stop when no more matches possible
```

**2. Row-best** (`strategy = "row_best"`, default):

- For each left unit, find best available right unit

- Process left units in order

- Fast and simple

- Medium quality

```
Algorithm:
1. For i = 1 to n_L:

   - Find unmatched right unit j with minimum distance to i

   - Assign (i, j)

2. Return all assignments
```

**3. Priority queue** (`strategy = "pq"`):

- Maintains priority queue of potential matches

- Updates dynamically as assignments made

- Best for very large problems

- Fastest option

```{r greedy-strategies}
# Compare greedy strategies on same data
set.seed(101)
test_left <- tibble(id = 1:200, x = rnorm(200))
test_right <- tibble(id = 1:200, x = rnorm(200))

strategies <- c("sorted", "row_best", "pq")
results <- list()

for (strat in strategies) {
  time <- system.time({
    result <- greedy_couples(
      test_left, test_right,
      vars = "x",
      strategy = strat
    )
  })

  results[[strat]] <- list(
    time = time["elapsed"],
    mean_dist = mean(result$pairs$distance),
    total_dist = result$info$total_distance
  )
}

# Display comparison
comparison <- do.call(rbind, lapply(names(results), function(s) {
  data.frame(
    strategy = s,
    time_sec = round(results[[s]]$time, 4),
    mean_distance = round(results[[s]]$mean_dist, 4),
    total_distance = round(results[[s]]$total_dist, 2)
  )
}))

print(comparison)
```

**Recommendation:**

- Default to `strategy = "row_best"` for good speed/quality balance

- Use `strategy = "sorted"` when quality is important but optimal is too slow

- Use `strategy = "pq"` for n > 10,000

## Caliper Constraints

Calipers impose maximum allowable match distances to ensure minimum quality.

### Why Use Calipers

Without calipers, optimal matching may pair very dissimilar units just to maximize the number of matches. Calipers prevent this by:

- Setting a distance threshold beyond which matches are forbidden

- Reducing the number of matches to improve average quality

- Implementing the "common support" requirement in propensity score matching

```{r calipers, fig.alt="Overlapping histograms comparing match distances with and without caliper constraint, showing the caliper excludes distant matches"}
# Create data where some units are far apart
set.seed(202)
left_cal <- tibble(
  id = 1:50,
  x = c(rnorm(40, mean = 0, sd = 1), rnorm(10, mean = 5, sd = 0.5))  # Some outliers
)
right_cal <- tibble(
  id = 1:50,
  x = rnorm(50, mean = 0, sd = 1)
)

# Match without caliper - pairs everything
result_no_cal <- match_couples(
  left_cal, right_cal,
  vars = "x",
  auto_scale = FALSE
)

# Match with caliper - excludes poor matches
result_with_cal <- match_couples(
  left_cal, right_cal,
  vars = "x",
  max_distance = 1.5,  # Caliper: max distance = 1.5
  auto_scale = FALSE
)

cat("Without caliper:\n")
cat("  Matched:", result_no_cal$info$n_matched, "\n")
cat("  Mean distance:", round(mean(result_no_cal$pairs$distance), 3), "\n")
cat("  Max distance:", round(max(result_no_cal$pairs$distance), 3), "\n\n")

cat("With caliper (1.5):\n")
cat("  Matched:", result_with_cal$info$n_matched, "\n")
cat("  Mean distance:", round(mean(result_with_cal$pairs$distance), 3), "\n")
cat("  Max distance:", round(max(result_with_cal$pairs$distance), 3), "\n")

# Visualize caliper effect
ggplot(result_no_cal$pairs, aes(x = distance)) +
  geom_histogram(aes(fill = "No caliper"), bins = 30, alpha = 0.5) +
  geom_histogram(
    data = result_with_cal$pairs,
    aes(fill = "With caliper"),
    bins = 30,
    alpha = 0.5
  ) +
  geom_vline(xintercept = 1.5, linetype = "dashed", color = "red") +
  labs(
    title = "Caliper Effect on Match Distances",
    x = "Distance",
    y = "Count",
    fill = "Condition"
  ) +
  theme_minimal() +
  theme(plot.background = element_rect(fill = "transparent", color = NA),
        panel.background = element_rect(fill = "transparent", color = NA),
        legend.background = element_rect(fill = "transparent", color = NA),
        panel.grid = element_blank())
```

### Choosing Caliper Width

Common approaches:

**1. Standard deviation rule**: Caliper = 0.1 to 0.25 pooled SD

```{r caliper-sd, eval=FALSE}
# Calculate pooled SD
combined <- bind_rows(
  left_data |> mutate(group = "left"),
  right_data |> mutate(group = "right")
)

pooled_sd <- sd(combined$age)  # For single variable
caliper_width <- 0.2 * pooled_sd

result <- match_couples(
  left_data, right_data,
  vars = "age",
  max_distance = caliper_width
)
```

**2. Propensity score rule**: 0.1 to 0.25 SD of propensity score logit

**3. Empirical rule**: Examine distance distribution, exclude extreme tail

```{r caliper-empirical}
# Fit all matches first
all_matches <- match_couples(left_cal, right_cal, vars = "x")

# Choose caliper at 90th percentile
caliper_90 <- quantile(all_matches$pairs$distance, 0.90)

# Refit with caliper
refined_matches <- match_couples(
  left_cal, right_cal,
  vars = "x",
  max_distance = caliper_90
)

cat("90th percentile caliper:", round(caliper_90, 3), "\n")
cat("Matches retained:",
    round(100 * refined_matches$info$n_matched / all_matches$info$n_matched, 1), "%\n")
```

## Blocking and Stratification

Blocking (exact matching on key variables) combined with distance matching on remaining covariates is a powerful strategy.

### Why Use Blocking

**Benefits:**

- Ensures exact balance on critical variables (site, gender, age category)

- Reduces problem size (smaller within-block matching problems)

- Prevents poor cross-block matches

- Transparent and interpretable

**When to use:**

- Strong domain knowledge about important stratification variables

- Variables that absolutely must be balanced (e.g., study site in multi-center trials)

- Large datasets where blocking reduces computational burden

### Exact Blocking with `matchmaker()`

```{r blocking-exact}
# Create multi-site data
set.seed(303)
multi_site <- bind_rows(
  tibble(
    id = 1:100,
    site = sample(c("A", "B", "C"), 100, replace = TRUE),
    age = rnorm(100, 50, 10),
    income = rnorm(100, 55000, 15000),
    group = "treatment"
  ),
  tibble(
    id = 101:250,
    site = sample(c("A", "B", "C"), 150, replace = TRUE),
    age = rnorm(150, 50, 10),
    income = rnorm(150, 55000, 15000),
    group = "control"
  )
)

left_site <- multi_site |> filter(group == "treatment")
right_site <- multi_site |> filter(group == "control")

# Create exact blocks by site
blocks <- matchmaker(
  left = left_site,
  right = right_site,
  block_type = "group",
  block_by = "site"
)

cat("Blocking structure:\n")
print(blocks$block_summary)

# Match within blocks
result_blocked <- match_couples(
  left = blocks$left,
  right = blocks$right,
  vars = c("age", "income"),
  block_id = "block_id",  # Use block IDs from matchmaker
  auto_scale = TRUE
)

# Verify exact site balance
result_blocked$pairs |>
  mutate(left_id = as.integer(left_id), right_id = as.integer(right_id)) |>
  left_join(left_site |> dplyr::select(id, site), by = c("left_id" = "id")) |>
  left_join(right_site |> dplyr::select(id, site), by = c("right_id" = "id"), suffix = c("_left", "_right")) |>
  count(site_left, site_right)
```

### Cluster-Based Blocking

For continuous variables, use k-means clustering to create blocks:

```{r blocking-cluster}
# Create blocks based on age groups (data-driven)
cluster_blocks <- matchmaker(
  left = left_site,
  right = right_site,
  block_type = "cluster",
  block_vars = "age",
  n_blocks = 3
)

cat("Cluster-based blocks:\n")
print(cluster_blocks$block_summary)

# Match within clusters
result_clustered <- match_couples(
  left = cluster_blocks$left,
  right = cluster_blocks$right,
  vars = c("age", "income"),
  block_id = "block_id",
  auto_scale = TRUE
)

# Show age distribution by cluster
cluster_blocks$left |>
  group_by(block_id) |>
  summarise(
    n = n(),
    mean_age = mean(age),
    sd_age = sd(age)
  )
```

## Balance Diagnostics

After matching, assess balance quality using `balance_diagnostics()`.

### Key Balance Metrics

**1. Standardized differences:**
$$
\text{Std Diff} = \frac{\bar{x}_{\text{left}} - \bar{x}_{\text{right}}}{\sqrt{(s_{\text{left}}^2 + s_{\text{right}}^2) / 2}}
$$

**Thresholds:**

- < 0.1: Excellent balance

- 0.1 - 0.25: Good balance

- 0.25 - 0.5: Acceptable (may need further adjustment)

- > 0.5: Poor balance (reconsider matching strategy)

**2. Variance ratios:**
$$
\text{VR} = \frac{s_{\text{left}}^2}{s_{\text{right}}^2}
$$

**Interpretation:**

- Close to 1.0: Similar variability (good)

- < 0.5 or > 2.0: Concerning imbalance in spread

**3. Kolmogorov-Smirnov tests:**

- Non-parametric test for distributional differences

- Sensitive to differences beyond mean/variance

- P-value > 0.05 suggests similar distributions

```{r balance-assessment}
# Perform matching
match_result <- match_couples(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  auto_scale = TRUE
)

# Get matched samples
matched_left <- left_data |>
  filter(id %in% match_result$pairs$left_id)

matched_right <- right_data |>
  filter(id %in% match_result$pairs$right_id)

# Compute balance diagnostics
balance <- balance_diagnostics(
  result = match_result,
  left = left_data,
  right = right_data,
  vars = c("age", "income")
)

# Print balance summary
print(balance)

# Extract balance table for reporting
balance_table(balance)
```

### How to Interpret Balance Results

The balance diagnostics output tells you whether your match succeeded. Here's how to read it:

```
Balance Diagnostics
-------------------
Variables: age, income

Variable Statistics:
  variable mean_left mean_right std_diff var_ratio ks_stat ks_p
  age      45.2      45.8       -0.08    0.95      0.06    0.89
  income   58000     56500      0.12     1.08      0.09    0.45
```

**Reading each column**:

| Column | Meaning | Good Values |
|--------|---------|-------------|
| `std_diff` | Standardized difference in means | \|value\| < 0.1 excellent, < 0.25 acceptable |
| `var_ratio` | Ratio of standard deviations | 0.5--2.0 acceptable, closer to 1.0 is better |
| `ks_stat` | Kolmogorov-Smirnov statistic | Smaller is better |
| `ks_p` | KS test p-value | > 0.05 suggests similar distributions |

**Quality thresholds** for standardized differences:

- **< 0.1**: Excellent balance (treat as successfully matched)

- **0.1-0.25**: Good balance (acceptable for most purposes)

- **0.25-0.5**: Marginal (consider refinement or sensitivity analysis)

- **> 0.5**: Poor balance (matching strategy needs revision)

**What to do if balance is poor**:

1. **Add more matching variables** that explain the imbalance

2. **Tighten calipers** (accept fewer matches but better quality)

3. **Try blocking** on the problematic variable

4. **Report and discuss** in limitations section

### Visualizing Balance

```{r balance-plots, fig.alt="Bar chart comparing standardized differences before and after matching, with threshold lines at 0.1 and 0.25 showing improved balance after matching"}
# Before-after balance plot
# (Requires creating pre-match balance for comparison)

# For pre-match comparison, just compute summary statistics directly
pre_match_stats <- tibble(
  variable = c("age", "income"),
  std_diff = c(
    (mean(left_data$age) - mean(right_data$age)) / sqrt((sd(left_data$age)^2 + sd(right_data$age)^2) / 2),
    (mean(left_data$income) - mean(right_data$income)) / sqrt((sd(left_data$income)^2 + sd(right_data$income)^2) / 2)
  ),
  when = "Before"
)

# Combine for plotting
balance_comparison <- bind_rows(
  pre_match_stats,
  balance$var_stats |> dplyr::select(variable, std_diff) |> mutate(when = "After")
)

ggplot(balance_comparison, aes(x = variable, y = std_diff, fill = when)) +
  geom_hline(yintercept = c(-0.1, 0.1), linetype = "dashed", color = "#93c54b", linewidth = 0.8) +
  geom_hline(yintercept = c(-0.25, 0.25), linetype = "dashed", color = "#f47c3c", linewidth = 0.8) +
  geom_col(position = "dodge", width = 0.5) +
  geom_label(aes(x = 1.5, y = -0.1, label = "±0.1 excellent"),
             fill = "#93c54b", color = "white", inherit.aes = FALSE,
             size = 3, fontface = "bold", label.padding = unit(0.2, "lines")) +
  geom_label(aes(x = 1.5, y = 0.25, label = "±0.25 acceptable"),
             fill = "#f47c3c", color = "white", inherit.aes = FALSE,
             size = 3, fontface = "bold", label.padding = unit(0.2, "lines")) +
  labs(
    title = "Covariate Balance Before and After Matching",
    x = "Variable",
    y = "Standardized Difference",
    fill = "Timing"
  ) +
  theme_minimal() +
  theme(plot.background = element_rect(fill = "transparent", color = NA),
        panel.background = element_rect(fill = "transparent", color = NA),
        legend.background = element_rect(fill = "transparent", color = NA),
        panel.grid = element_blank()) +
  coord_flip()
```

## Real-World Example: Treatment Effect Estimation

Complete workflow for estimating treatment effects in an observational study.

### Scenario

Evaluate the effect of a job training program on earnings. Participants self-selected into the program, creating potential selection bias.

**Data:**

- Treatment: 200 program participants

- Control: 500 non-participants

- Covariates: age, education, prior earnings, employment status

- Outcome: Earnings one year after program

### Step 1: Data Preparation

```{r real-world-data}
set.seed(404)

# Simulate realistic scenario with selection bias
# Program attracts younger, more educated, currently employed individuals
create_participant <- function(n, is_treatment) {
  if (is_treatment) {
    tibble(
      id = 1:n,
      age = rnorm(n, mean = 35, sd = 8),
      education_years = rnorm(n, mean = 14, sd = 2),
      prior_earnings = rnorm(n, mean = 35000, sd = 10000),
      employed = sample(c(0, 1), n, replace = TRUE, prob = c(0.3, 0.7)),
      treatment = 1
    )
  } else {
    tibble(
      id = (n+1):(n+500),
      age = rnorm(500, mean = 42, sd = 12),
      education_years = rnorm(500, mean = 12, sd = 3),
      prior_earnings = rnorm(500, mean = 30000, sd = 12000),
      employed = sample(c(0, 1), 500, replace = TRUE, prob = c(0.5, 0.5)),
      treatment = 0
    )
  }
}

treatment_group <- create_participant(200, TRUE)
control_group <- create_participant(500, FALSE)

# Simulate outcome (earnings) with treatment effect
# True effect: +$5,000, with heterogeneity
treatment_group <- treatment_group |>
  mutate(
    earnings = prior_earnings +
      5000 +  # True treatment effect
      2000 * rnorm(n()) +  # Random variation
      100 * education_years  # Education effect
  )

control_group <- control_group |>
  mutate(
    earnings = prior_earnings +
      2000 * rnorm(n()) +
      100 * education_years
  )

# Examine baseline imbalance
cat("Pre-matching differences:\n")
cat("Age diff:",
    mean(treatment_group$age) - mean(control_group$age), "\n")
cat("Education diff:",
    mean(treatment_group$education_years) - mean(control_group$education_years), "\n")
cat("Prior earnings diff:",
    mean(treatment_group$prior_earnings) - mean(control_group$prior_earnings), "\n")
```

### Step 2: Perform Matching

```{r real-world-matching}
# Match on baseline covariates
job_match <- match_couples(
  left = treatment_group,
  right = control_group,
  vars = c("age", "education_years", "prior_earnings", "employed"),
  auto_scale = TRUE,
  scale = "robust",
  return_diagnostics = TRUE
)

cat("Matching summary:\n")
cat("  Treated units:", nrow(treatment_group), "\n")
cat("  Matched treated:", job_match$info$n_matched, "\n")
cat("  Match rate:",
    round(100 * job_match$info$n_matched / nrow(treatment_group), 1), "%\n")
```

### Step 3: Assess Balance

```{r real-world-balance}
# Extract matched samples
matched_treated <- treatment_group |>
  filter(id %in% job_match$pairs$left_id)

matched_control <- control_group |>
  filter(id %in% job_match$pairs$right_id)

# Compute balance
job_balance <- balance_diagnostics(
  result = job_match,
  left = treatment_group,
  right = control_group,
  vars = c("age", "education_years", "prior_earnings", "employed")
)

print(job_balance)

# Check overall balance quality
cat("\nOverall balance:\n")
cat("  Mean |std diff|:", round(job_balance$overall$mean_abs_std_diff, 3), "\n")
cat("  Max |std diff|:", round(job_balance$overall$max_abs_std_diff, 3), "\n")
cat("  % with |std diff| > 0.1:",
    round(job_balance$overall$pct_large_imbalance, 1), "%\n")
```

### Step 4: Estimate Treatment Effect

```{r real-world-ate}
# Naive estimate (without matching) - BIASED
naive_effect <- mean(treatment_group$earnings) - mean(control_group$earnings)

# Matched estimate - accounts for baseline differences
matched_effect <- mean(matched_treated$earnings) - mean(matched_control$earnings)

# Paired t-test for significance
paired_comparison <- tibble(
  treated = matched_treated$earnings,
  control = matched_control$earnings[match(
    matched_treated$id,
    job_match$pairs$left_id
  )]
)

t_test <- t.test(paired_comparison$treated, paired_comparison$control, paired = TRUE)

# Report results
cat("Treatment Effect Estimates:\n\n")
cat("Naive (unmatched):\n")
cat("  Difference: $", round(naive_effect, 0), "\n")
cat("  (Upward biased due to selection)\n\n")

cat("Matched estimate:\n")
cat("  Difference: $", round(matched_effect, 0), "\n")
cat("  95% CI: ($", round(t_test$conf.int[1], 0), ", $",
    round(t_test$conf.int[2], 0), ")\n")
cat("  P-value:", format.pval(t_test$p.value, digits = 3), "\n")
cat("  (Closer to true effect of $5,000)\n")
```

### Step 5: Publication-Ready Output

```{r real-world-table, eval=FALSE}
# Table 1: Balance table
balance_publication <- balance_table(job_balance)
print(balance_publication)

# Table 2: Sample characteristics
sample_table <- bind_rows(
  matched_treated |>
    summarise(
      Group = "Treatment",
      N = n(),
      `Age (mean ± SD)` = sprintf("%.1f ± %.1f", mean(age), sd(age)),
      `Education (years)` = sprintf("%.1f ± %.1f", mean(education_years), sd(education_years)),
      `Prior Earnings` = sprintf("$%s ± %s",
                                 format(round(mean(prior_earnings)), big.mark = ","),
                                 format(round(sd(prior_earnings)), big.mark = ",")),
      `Employed (%)` = sprintf("%.1f", 100 * mean(employed))
    ),
  matched_control |>
    summarise(
      Group = "Control",
      N = n(),
      `Age (mean ± SD)` = sprintf("%.1f ± %.1f", mean(age), sd(age)),
      `Education (years)` = sprintf("%.1f ± %.1f", mean(education_years), sd(education_years)),
      `Prior Earnings` = sprintf("$%s ± %s",
                                 format(round(mean(prior_earnings)), big.mark = ","),
                                 format(round(sd(prior_earnings)), big.mark = ",")),
      `Employed (%)` = sprintf("%.1f", 100 * mean(employed))
    )
)

print(sample_table)

# Table 3: Treatment effect
effect_table <- tibble(
  Method = c("Unmatched", "Matched"),
  `N (Treated)` = c(nrow(treatment_group), nrow(matched_treated)),
  `N (Control)` = c(nrow(control_group), nrow(matched_control)),
  `Effect Estimate` = sprintf("$%s", format(round(c(naive_effect, matched_effect)), big.mark = ",")),
  `95% CI` = c("--", sprintf("($%s, $%s)",
                            format(round(t_test$conf.int[1]), big.mark = ","),
                            format(round(t_test$conf.int[2]), big.mark = ",")))
)

print(effect_table)
```

## Performance Considerations

### Scalability

**Optimal matching complexity:** $O(n^3)$ using Jonker-Volgenant

- n = 100: < 0.01 seconds

- n = 500: ~ 0.1 seconds

- n = 1,000: ~ 1 second

- n = 3,000: ~ 10 seconds

- n = 5,000: ~ 30-60 seconds

**Greedy matching complexity:** $O(n^2 \log n)$ for sorted, $O(n^2)$ for row-best

- n = 5,000: ~ 1 second

- n = 10,000: ~ 3-5 seconds

- n = 50,000: ~ 30-60 seconds

### Memory Usage

- Cost matrix: 8n² bytes (for n×n problem)

- n = 1,000: ~ 8 MB

- n = 5,000: ~ 200 MB

- n = 10,000: ~ 800 MB

For very large problems:

1. Use greedy matching to avoid full cost matrix

2. Use blocking to reduce within-block size

3. Consider approximate methods (upcoming vignette)

### Optimization Tips

**1. Use blocking for large datasets**

```{r optimization-blocking, eval=FALSE}
# Instead of matching 10,000 × 10,000:
# Create 10 blocks of ~1,000 × 1,000 each
blocks <- matchmaker(
  left_large, right_large,
  block_type = "cluster",
  cluster_vars = "age",
  n_clusters = 10
)

# Much faster: 10 * O(1000^3) << O(10000^3)
result <- match_couples(
  blocks$left, blocks$right,
  vars = covariates,
  block_id = "block_id"
)
```

**2. Start with greedy, refine if needed**

```{r optimization-greedy, eval=FALSE}
# Quick greedy match for exploration
quick <- greedy_couples(
  left_data, right_data,
  vars = covariates,
  strategy = "row_best"
)

# Assess balance
balance_quick <- balance_diagnostics(quick, left_data, right_data, vars = covariates)

# If balance is acceptable, done!
# If not, try optimal or add blocking
```

**3. Use calipers to reduce problem size**

```{r optimization-caliper, eval=FALSE}
# Caliper removes distant pairs from cost matrix
# Can dramatically reduce effective problem size
result <- match_couples(
  left_data, right_data,
  vars = covariates,
  max_distance = 0.25,  # Strict caliper
  auto_scale = TRUE
)
```

## What Can Go Wrong

Matching doesn't always succeed. Here are common problems and solutions.

### Problem: Poor Balance Despite Matching

**Symptom**: `balance_diagnostics()` shows |std_diff| > 0.25 for some variables.

**Causes**:
- Groups are fundamentally too different (weak overlap)

- Important confounders not included in matching variables

- Caliper too loose

**Solutions**:
```{r poor-balance-fix, eval=FALSE}
# 1. Add more matching variables
result <- match_couples(left, right,
                        vars = c("age", "income", "education", "region"),  # Added!
                        auto_scale = TRUE)

# 2. Tighten caliper (fewer but better matches)
result <- match_couples(left, right, vars = vars,
                        max_distance = 0.1)  # Was 0.5

# 3. Block on the problematic variable
blocks <- matchmaker(left, right, block_type = "group", block_by = "region")
result <- match_couples(blocks$left, blocks$right, vars = other_vars,
                        block_id = "block_id")
```

### Problem: Very Few Matches

**Symptom**: `n_matched` is much smaller than `nrow(left)`.

**Causes**:
- Caliper too strict

- Non-overlapping covariate distributions

- Blocking creates small strata

**Diagnosis**:
```{r few-matches-diagnosis, eval=FALSE}
# Check covariate overlap
library(ggplot2)
combined <- bind_rows(
  left |> mutate(group = "treatment"),
  right |> mutate(group = "control")
)
ggplot(combined, aes(x = age, fill = group)) +
  geom_density(alpha = 0.5) +
  labs(title = "Check for Overlap")
```

**Solutions**:
- Relax caliper

- Use coarser blocking categories

- Accept that some treatment units are unmatchable (report this!)

### Problem: Matching Takes Too Long

**Symptom**: `match_couples()` runs for minutes or doesn't complete.
**Cause**: $O(n^3)$ complexity for optimal matching.

**Solutions**:
```{r slow-matching-fix, eval=FALSE}
# For n > 3000: use greedy
result <- greedy_couples(left, right, vars = vars, strategy = "sorted")

# For n > 5000: add blocking
blocks <- matchmaker(left, right, block_type = "cluster", n_blocks = 20)
result <- match_couples(blocks$left, blocks$right, vars = vars,
                        block_id = "block_id")
```

### Problem: Memory Error

**Symptom**: R crashes or reports "cannot allocate vector of size X".

**Cause**: Full cost matrix doesn't fit in RAM. A 10,000×10,000 matrix needs ~800 MB.

**Solutions**:
- Use `greedy_couples()` which doesn't require full matrix

- Use blocking to create smaller sub-problems

- Consider random sampling if sample size permits

## Summary

This vignette walked through a complete matching workflow using the job training evaluation example:

1. **Problem framing**: Treatment effect estimation with selection bias

2. **Matching**: Creating comparable groups with `match_couples()`

3. **Preprocessing**: Automatic scaling and variable health checks

4. **Assessment**: Balance diagnostics and interpretation

5. **Refinement**: Calipers, blocking, and greedy alternatives

6. **Estimation**: Treatment effect with confidence intervals

**Key Takeaways**:

| Concept | Key Point |
|---------|-----------|
| Preprocessing | Always use `auto_scale = TRUE` unless you have a specific reason not to |
| Balance | Target \|std_diff\| < 0.1; accept < 0.25 |
| Calipers | Start loose, tighten if needed for balance |
| Blocking | Use for exact balance on key categorical variables |
| Greedy | Use for n > 3000; almost as good, much faster |

**Recommended Workflow**:

```{r workflow-diagram, fig.width=8, fig.height=6, echo=FALSE, fig.alt="Flowchart showing recommended matching workflow with iterative refinement loop"}
library(ggplot2)

# Define workflow nodes
nodes <- data.frame(
  label = c("1. Explore data\nIdentify confounders",
            "2. First match\nmatch_couples()",
            "3. Check balance\nbalance_diagnostics()",
            "Balance\nOK?",
            "4. Estimate\neffect",
            "Refine: caliper,\nblocking, more vars"),
  x = c(4, 4, 4, 4, 2, 6),
  y = c(6, 5, 4, 3, 2, 2),
  type = c("step", "step", "step", "decision", "step", "step")
)

# Define edges
edges <- data.frame(
  from_x = c(4, 4, 4, 4, 4, 6),
  from_y = c(6, 5, 4, 3, 3, 2),
  to_x = c(4, 4, 4, 2, 6, 4),
  to_y = c(5, 4, 3, 2, 2, 4),
  label = c("", "", "", "Yes", "No", ""),
  label_x = c(NA, NA, NA, 2.8, 5.2, 5.5),
  label_y = c(NA, NA, NA, 2.6, 2.6, 3)
)

ggplot() +
  # Draw straight edges (vertical arrows)
  geom_segment(data = edges[1:3, ],
               aes(x = from_x, y = from_y, xend = to_x, yend = to_y),
               arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
               color = "#3e3f3a", linewidth = 0.7) +

  # Draw decision branches
  geom_segment(data = edges[4:5, ],
               aes(x = from_x, y = from_y, xend = to_x, yend = to_y),
               arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
               color = "#3e3f3a", linewidth = 0.7) +
  # Draw return loop (curved)
  geom_curve(data = edges[6, ],
             aes(x = from_x, y = from_y, xend = to_x, yend = to_y),
             arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
             color = "#3e3f3a", linewidth = 0.7, curvature = 0.3) +
  # Edge labels - no explicit color so CSS can control dark mode
  geom_text(data = edges[!is.na(edges$label_x), ],
            aes(x = label_x, y = label_y, label = label),
            size = 3, fontface = "italic") +
  # Step nodes (rectangles) - sandstone info color with white text
  geom_label(data = nodes[nodes$type == "step", ],
             aes(x = x, y = y, label = label), size = 3,
             fill = "#29abe0", color = "white",
             label.padding = unit(0.4, "lines"), label.r = unit(0.15, "lines"),
             lineheight = 0.9) +
  # Decision node (diamond) - sandstone warning color
  geom_point(data = nodes[nodes$type == "decision", ],
             aes(x = x, y = y), shape = 23, size = 20,
             fill = "#ffc107", color = "#f47c3c", stroke = 1.5) +
  # Decision text - use default color that adapts to dark mode
  geom_text(data = nodes[nodes$type == "decision", ],
            aes(x = x, y = y, label = label), size = 3, lineheight = 0.85) +
  # Title
  labs(title = "Matching Workflow") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 12),
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank(),
        plot.background = element_rect(fill = "transparent", color = NA),
        panel.background = element_rect(fill = "transparent", color = NA)) +
  coord_cartesian(xlim = c(0, 8), ylim = c(1.5, 6.5))
```

---

## Advanced Matching Techniques

### Full Matching (Variable-Ratio Groups)

Standard one-to-one matching discards unmatched units — a significant loss when control pools are large. Full matching assigns *every* unit to a matched group with variable ratios (1:k or k:1), maximizing sample size while preserving balance.

```{r full-matching}
# Full matching: every unit gets assigned to a group
result_full <- full_match(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  method = "optimal"   # min-cost max-flow (default)
)

result_full
```

The `method` argument controls the algorithm:

- `"optimal"` (default) — Solves a min-cost max-flow problem that minimizes total distance across all group assignments simultaneously. Uses Dijkstra's algorithm with Johnson potentials. Guarantees the globally optimal solution.

- `"greedy"` — A fast two-pass heuristic: (1) each left unit picks its nearest right unit, (2) remaining right units are assigned to their nearest matched left unit. Faster but not globally optimal.

```{r full-matching-comparison}
# Compare optimal vs greedy
result_full_greedy <- full_match(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  method = "greedy"
)

cat("Optimal: ", result_full$info$n_groups, "groups,",
    result_full$info$n_unmatched_left, "unmatched left,",
    result_full$info$n_unmatched_right, "unmatched right\n")
cat("Greedy:  ", result_full_greedy$info$n_groups, "groups,",
    result_full_greedy$info$n_unmatched_left, "unmatched left,",
    result_full_greedy$info$n_unmatched_right, "unmatched right\n")
```

Full matching supports `min_controls` and `max_controls` to constrain group sizes, and `caliper` to exclude poor matches:

```{r full-matching-constraints}
# Constrain: each group gets 2-5 right units, caliper of 1.5
result_constrained <- full_match(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  min_controls = 2,
  max_controls = 5,
  caliper = 1.5
)

cat("Groups:", result_constrained$info$n_groups, "\n")
cat("Unmatched left:", result_constrained$info$n_unmatched_left, "\n")
cat("Unmatched right:", result_constrained$info$n_unmatched_right, "\n")
```

The output is an S3 object of class `full_matching_result` with a `groups` tibble containing `group_id`, `id`, `side`, and `weight`. Within each group, right-unit weights sum to the number of left units, enabling unbiased estimation:

```{r full-matching-weights}
head(result_full$groups, 10)
```

**When to use full matching vs one-to-one:**

| Criterion | One-to-one (`match_couples`) | Full (`full_match`) |
|-----------|------------------------------|---------------------|
| Sample size | Discards excess units | Uses every unit |
| Estimand | ATT (treatment on treated) | ATT or ATE (with weights) |
| Group structure | Pairs (1:1) | Variable-ratio groups |
| Balance | Excellent within pairs | Good overall (weighted) |
| Precision | Lower (smaller n) | Higher (larger effective n) |

### Ratio Matching (k:1)

Sometimes matching multiple controls per treated unit improves precision. Use the `ratio` parameter:

```{r ratio-matching}
# Match 2 controls per treated unit
result_2to1 <- match_couples(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  auto_scale = TRUE,
  ratio = 2,
  method = "hungarian"
)

result_2to1$pairs
```

Higher ratios increase sample size at the cost of slightly worse individual match quality. A common choice is 2:1 or 3:1.

### With-Replacement Matching

When controls are scarce relative to treated units, allowing replacement lets each control serve as a match for multiple treated units:

```{r replacement-matching}
# Allow controls to be reused
result_wr <- match_couples(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  auto_scale = TRUE,
  replace = TRUE,
  method = "hungarian"
)

# Some controls may appear multiple times
n_distinct_controls <- length(unique(result_wr$pairs$right_id))
cat("Unique controls used:", n_distinct_controls, "out of", nrow(right_data), "\n")
```

With-replacement matching reduces bias (each treated unit gets its closest match) but increases variance.

### Propensity Score Matching

Instead of matching directly on covariates, you can match on the estimated probability of treatment — the propensity score. `ps_match()` automates this:

```{r ps-matching}
# Combine data for propensity score estimation
combined <- rbind(
  transform(left_data[, c("id", "age", "income")], treated = 1L),
  transform(right_data[, c("id", "age", "income")], treated = 0L)
)

# Match on propensity scores (wider caliper for this example)
ps_result <- ps_match(
  formula = treated ~ age + income,
  data = combined,
  treatment = "treated",
  caliper_sd = 0.5,
  method = "hungarian"
)

ps_result
```

The function fits a logistic regression, transforms scores to the logit scale, and calls `match_couples()` with a caliper constraint. The default caliper is 0.2 SD of logit(PS) (Rosenbaum and Rubin recommendation); we use 0.5 here for this small simulated dataset. You can also supply a pre-fitted model via the `ps_model` argument.

### Cardinality Matching

Standard matching minimizes total distance. Cardinality matching instead maximizes the number of matched pairs while enforcing strict balance constraints:

```{r cardinality-matching}
# Maximize pairs subject to balance threshold
card_result <- cardinality_match(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  max_std_diff = 0.1,  # Excellent balance threshold
  auto_scale = TRUE,
  method = "hungarian"
)

cat("Pairs matched:", card_result$info$n_matched, "\n")
cat("Pruning iterations:", card_result$info$pruning_iterations, "\n")
cat("Pairs removed:", card_result$info$pairs_removed, "\n")
```

This is useful when you need strict balance guarantees and are willing to sacrifice some sample size.

### Coarsened Exact Matching (CEM)

CEM coarsens continuous variables into bins, then performs exact matching within strata. This avoids model dependence — results don't rely on a distance function or propensity score model:

```{r cem-matching}
cem_result <- cem_match(
  left = left_data,
  right = right_data,
  vars = c("age", "income"),
  n_bins = "sturges"   # automatic bin width; also "fd", "scott", or an integer
)

cem_result

# Strata summary: how many units in each coarsened cell
head(cem_result$strata_summary)
```

You can supply custom cutpoints per variable with the `cutpoints` argument, or pass categorical grouping variables with `grouping`. Each matched unit receives a weight inversely proportional to the stratum size, ensuring balance across strata.

### Subclassification

Subclassification (stratification on the propensity score) divides the sample into strata of similar PS, then computes within-stratum effects. Unlike one-to-one matching, no units are discarded:

```{r subclass-matching}
# Combine data
combined <- rbind(
  transform(left_data[, c("id", "age", "income")], treated = 1L),
  transform(right_data[, c("id", "age", "income")], treated = 0L)
)

sub_result <- subclass_match(
  formula = treated ~ age + income,
  data = combined,
  treatment = "treated",
  n_subclasses = 5,
  estimand = "ATT"
)

sub_result
head(sub_result$subclass_summary)
```

### Ecosystem Integration

All couplr result types can be converted to a unified analysis-ready format or to MatchIt-class objects for interoperability with cobalt and marginaleffects.

**`match_data()`** produces a single data frame with `treatment`, `weights`, `subclass`, and `distance` columns — ready for downstream modelling:

```{r match-data-example}
md <- couplr::match_data(result, left_data, right_data)
head(md)
```

**`as_matchit()`** constructs a `matchit`-class object, enabling direct use with `cobalt::bal.tab()`, `cobalt::love.plot()`, and `marginaleffects::avg_comparisons()`:
```{r as-matchit-example, eval=FALSE}
mi <- as_matchit(result, left_data, right_data)

# cobalt balance table
cobalt::bal.tab(mi)

# marginaleffects
marginaleffects::avg_comparisons(model, vcov = "HC3", newdata = match.data(mi))
```

---

## Sensitivity Analysis

After matching, a natural question is: *how much hidden bias would be needed to invalidate the results?* Rosenbaum bounds address this by computing p-values under varying degrees of unobserved confounding.

```{r sensitivity-analysis}
# Simulate a modest treatment effect (small enough that hidden bias matters)
set.seed(99)
n_sa <- 50
left_sa <- data.frame(
  id = 1:n_sa,
  x = rnorm(n_sa, 5, 2),
  outcome = rnorm(n_sa, mean = 1.0, sd = 2)
)
right_sa <- data.frame(
  id = (n_sa + 1):(2 * n_sa),
  x = rnorm(n_sa, 5, 2),
  outcome = rnorm(n_sa, mean = 0.3, sd = 2)
)

# Match on covariate x
result_sa <- match_couples(
  left = left_sa,
  right = right_sa,
  vars = "x",
  method = "hungarian"
)

# How robust is the result to hidden bias?
sa <- sensitivity_analysis(
  result = result_sa,
  left = left_sa,
  right = right_sa,
  outcome_var = "outcome",
  gamma = seq(1, 2, by = 0.25)
)

sa
```

The **critical gamma** ($\Gamma$) is the smallest value at which the upper-bound p-value crosses 0.05. A larger critical gamma means the finding is more robust: a critical gamma of 1.75, for example, means an unobserved confounder would need to change the odds of treatment by a factor of 1.75 to explain away the result.

---

## Visualizing Results with autoplot

If `ggplot2` is available, `autoplot()` methods produce publication-ready plots:

```{r autoplot-examples, fig.width=7, fig.height=4}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)

  # Distance distribution
  autoplot(result, type = "histogram")
}
```

```{r autoplot-balance, fig.width=7, fig.height=4}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  # Balance diagnostics love plot
  bal <- balance_diagnostics(result, left_data, right_data, vars = c("age", "income"))
  autoplot(bal, type = "love")
}
```

```{r autoplot-sensitivity, fig.width=7, fig.height=4}
if (requireNamespace("ggplot2", quietly = TRUE)) {
  # Sensitivity analysis curve
  autoplot(sa)
}
```

All `autoplot()` methods return `ggplot` objects, so you can add layers, themes, and labels as needed.

---

## See Also

- `vignette("getting-started")` - Basic LAP solving

- `vignette("algorithms")` - Mathematical foundations

- `vignette("comparison")` - How couplr compares to MatchIt, optmatch, designmatch

- `vignette("troubleshooting")` - Common issues and solutions

- `vignette("pixel-morphing")` - Large-scale approximation strategies

- `?match_couples`, `?greedy_couples`, `?full_match`, `?balance_diagnostics`, `?matchmaker`

- `?ps_match`, `?cardinality_match`, `?sensitivity_analysis`, `?cem_match`, `?subclass_match`

- `?match_data`, `?as_matchit` — ecosystem integration
