Produces a detailed summary of the MSD estimation results, including the coefficient table with z-statistics and p-values, sample size information, and interpretation guidance.
Usage
# S3 method for class 'msd_result'
summary(object, ...)Examples
obs_df <- data.frame(
Y = rnorm(100), S0 = rnorm(100), S1 = rnorm(100),
D = rep(c(1, 0), each = 50)
)
unobs_df <- data.frame(
S0 = rnorm(200), S1 = rnorm(200), D = rep(c(1, 0), each = 100)
)
msd <- msd_data(observed = obs_df, unobserved = unobs_df)
result <- msd_dt_dip(msd, seed = 1)
summary(result)
#>
#> Mixed-Subjects Design: Treatment Effect Estimation
#> ===================================================
#>
#> Estimator: D-T DiP (cross-fit, K=2)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> ATE -0.0704 0.1940 -0.363 0.7166
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> 95%Confidence Interval: [-0.4507, 0.3098]
#>
#> Tuning Parameters:
#> lambda_1 (treatment): -0.0902
#> lambda_0 (control): -0.0019
#>
#> Sample Sizes:
#> Observed (human subjects): 100
#> Treated (n1): 50
#> Control (n0): 50
#> Unobserved (predictions only): 200
#> Total units: 300
#>
#> Interpretation:
#> The estimated effect is 0.0704 units, but this is not statistically significant (p = 0.717).
#> Using 200 unlabeled predictions improved estimation precision.
#>