Causal-inference diagnostics (R parity)

Balance, overlap, SUTVA-style assumption checks, and the median causal effect estimator. R parity of morie.mrm_diagnostics.

Value

Each diagnostic callable returns a named list of balance and overlap statistics (or the estimated effect) together with a plain-language interpretation.

References

Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social and Biomedical Sciences. Cambridge University Press. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33-38. Cole, S. R., & Hernan, M. A. (2008). Constructing inverse probability weights for marginal structural models. AJE, 168(6), 656-664.

Examples

set.seed(2026)
n <- 200L
df <- data.frame(
  D = rbinom(n, 1, 0.4),
  age = rnorm(n, 50, 10), bmi = rnorm(n, 27, 4)
)
mrm_standardised_difference(df,
  treatment_col = "D",
  covariates = c("age", "bmi")
)
#>   covariate mean_treated mean_control pooled_sd smd_pct imbalanced
#> 1       age      51.9585      50.6901    9.0353   14.04       TRUE
#> 2       bmi      27.2731      27.0571    4.5032    4.80      FALSE