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Estimate the ATE via the Interactive Regression Model (IRM)

Source: R/irm.R
morie_estimate_irm.Rd

Native rmorie implementation of the interactive regression model (cross-fit logistic propensity + GCV-ridge outcome regressions, AIPW orthogonal score), mirroring the Python sibling morie.estimate_irm(). Cross-validated against DoubleML in the package's cross tests; no DoubleML at runtime.

Usage

morie_estimate_irm(
  data,
  treatment,
  outcome,
  covariates,
  n_folds = 5L,
  random_state = 42L
)

Arguments

data

A data.frame containing outcome, treatment, and covariates.

treatment

Column name of the binary treatment.

outcome

Column name of the outcome.

covariates

Character vector of covariate column names.

n_folds

Number of cross-fitting folds (default 5).

random_state

Random seed (default 42).

Value

A list with components: ate, se, ci_lower, ci_upper, n, method ("IRM (rmorie native)").

Details

Following the DoubleML R package's own conventions, this uses the mlr3 ecosystem for the nuisance learners (ml_g for \(E[Y|T,X]\) and ml_m for \(P(T=1|X)\)). Defaults are lrn("regr.lm") and lrn("classif.log_reg"), which require nothing beyond stats. For higher-capacity defaults, install ranger and pass lrn("regr.ranger") / lrn("classif.ranger") via the underlying DoubleML::DoubleMLIRM$new() directly.

Following Chernozhukov et al. (2018), the IRM extends the partially linear model by allowing fully heterogeneous treatment effects: $$Y = g_0(T, X) + U,\quad E[U|T,X] = 0$$ $$T = m_0(X) + V,\quad E[V|X] = 0$$

CRAN Suggests

Runs on base R alone — no suggested packages required. If any are unavailable, the function raises an informative error.

References

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68. doi:10.1111/ectj.12097

Bach, P., Chernozhukov, V., Kurz, M. S., & Spindler, M. (2024). DoubleML – An object-oriented implementation of double machine learning in R. Journal of Statistical Software, 108(3). doi:10.18637/jss.v108.i03

Examples

set.seed(1)
n <- 200
X <- matrix(rnorm(n * 5), n, 5)
ps <- plogis(X[, 1] - X[, 2])
T <- rbinom(n, 1, ps)
Y <- 0.5 * T + X[, 1] + rnorm(n)
df <- data.frame(Y = Y, T = T, X)
morie_estimate_irm(df,
  treatment = "T", outcome = "Y",
  covariates = paste0("X", 1:5)
)
#> $ate
#> [1] -0.03720533
#> 
#> $se
#> [1] 0.2245752
#> 
#> $ci_lower
#> [1] -0.4773646
#> 
#> $ci_upper
#> [1] 0.402954
#> 
#> $n
#> [1] 200
#> 
#> $method
#> [1] "IRM (rmorie native)"
#>