couplr (version 1.0.10)

balance_diagnostics: Balance Diagnostics for Matched Pairs

Description

Computes comprehensive balance statistics comparing the distribution of matching variables between left and right units in the matched sample.

Usage

balance_diagnostics(
  result,
  left,
  right,
  vars = NULL,
  left_id = "id",
  right_id = "id"
)

Value

An S3 object of class balance_diagnostics containing:

var_stats

Tibble with per-variable balance statistics

overall

List with overall balance metrics

pairs

Tibble of matched pairs with variables

n_matched

Number of matched pairs

n_unmatched_left

Number of unmatched left units

n_unmatched_right

Number of unmatched right units

method

Matching method used

has_blocks

Whether blocking was used

block_stats

Per-block statistics (if blocking used)

Arguments

result

A matching result object from match_couples() or greedy_couples()

left

Data frame of left units

right

Data frame of right units

vars

Character vector of variable names to check balance for. Defaults to the variables used in matching (if available in result).

left_id

Character, name of ID column in left data (default: "id")

right_id

Character, name of ID column in right data (default: "id")

Details

This function computes several balance metrics:

Standardized Difference: The difference in means divided by the pooled standard deviation. Values less than 0.1 indicate excellent balance, 0.1-0.25 good balance.

Variance Ratio: The ratio of standard deviations (left/right). Values close to 1 are ideal.

KS Statistic: Kolmogorov-Smirnov test statistic comparing distributions. Lower values indicate more similar distributions.

Overall Metrics include mean absolute standardized difference across all variables, proportion of variables with large imbalance (|std diff| > 0.25), and maximum standardized difference.

Examples

# Create sample data
set.seed(123)
left <- data.frame(
  id = 1:10,
  age = rnorm(10, 45, 10),
  income = rnorm(10, 50000, 15000)
)
right <- data.frame(
  id = 11:30,
  age = rnorm(20, 47, 10),
  income = rnorm(20, 52000, 15000)
)

# Match
result <- match_couples(left, right, vars = c("age", "income"))

# Get balance diagnostics
balance <- balance_diagnostics(result, left, right, vars = c("age", "income"))
print(balance)

# Get balance table
balance_table(balance)