R/unihtee

Univariate Heterogeneous Treatment Effect Estimation

Author: Philippe Boileau

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unihtee provides tools for uncovering treatment effect modifiers in high-dimensional data. Treatment effect modification is defined using variable importance parameters based on absolute and relative effects. These parameters are called treatment effect modifier variable importance parameters (TEM-VIPs). Inference about TEM-VIPs is performed using causal machine learning estimators. Under general conditions, these estimators are unbiased and asymptotically linear, permitting straightforward hypothesis testing about TEM-VIPs.

Additional details about this methodology are provided in Boileau et al. (2022), Boileau et al. (2025), and in the package’s vignette.

Installation

The package may be installed from GitHub using remotes:

remotes::install_github("insightsengineering/unihtee")

unihtee is under active development. Check back often for updates.

Usage

unihtee() performs inference about potential effect modifiers’ TEM-VIPs. These parameters are defined for data-generating processes with continuous, binary and time-to-event outcomes with binary exposure variables. Absolute- and relative-scale TEM-VIPs are available. Details are provided in the vignette.

Example

We simulate some observational study data that contains ten pre-treatment covariates, of which are two treatment effect modifiers. We then perform inference about the absolute TEM-VIPs. Pre-treatment covariates with TEM-VIPs that are significantly different from zero suggest that these covariates modify the effect of treatment with respect to the average treatment effect.

library(unihtee)
library(MASS)
library(data.table)
library(sl3)

set.seed(510)

# create the dataset
n_obs <- 500
w <- mvrnorm(n = n_obs, mu = rep(0, 10), Sigma = diag(10))
confounder_names <- paste0("w_", seq_len(10))
colnames(w) <- confounder_names
a <- rbinom(n = n_obs, size = 1, prob = plogis(w[, 1] + w[, 2]))
y <- rnorm(n = n_obs, mean = w[, 1] + w[, 2] + a * w[, 3] - a * w[, 4])
dt <- as.data.table(cbind(w, a, y))

# estimate pre-treatment covariates' absolute TEM-VIPs
unihtee(
  data = dt,
  confounders = confounder_names,
  modifiers = confounder_names,
  exposure = "a",
  outcome = "y",
  outcome_type = "continuous",
  effect = "absolute"
)
#>     modifier     estimate        se           z      p_value    ci_lower
#>       <fctr>        <num>     <num>       <num>        <num>       <num>
#>  1:      w_3  1.044592804 0.1599527  6.53063474 6.549161e-11  0.73108547
#>  2:      w_4 -0.869002514 0.1505155 -5.77351005 7.763697e-09 -1.16401281
#>  3:      w_8  0.137803254 0.1138565  1.21032372 2.261547e-01 -0.08535554
#>  4:      w_1  0.115258422 0.1168820  0.98610900 3.240796e-01 -0.11383036
#>  5:      w_9  0.124150185 0.1295567  0.95826884 3.379272e-01 -0.12978102
#>  6:     w_10 -0.097928234 0.1356345 -0.72200119 4.702937e-01 -0.36377176
#>  7:      w_6  0.054845105 0.1157812  0.47369617 6.357166e-01 -0.17208602
#>  8:      w_2 -0.064478504 0.1761998 -0.36593963 7.144101e-01 -0.40983019
#>  9:      w_7 -0.014704981 0.1485610 -0.09898279 9.211519e-01 -0.30588450
#> 10:      w_5  0.001500152 0.1100365  0.01363321 9.891226e-01 -0.21417147
#>       ci_upper  p_value_fdr
#>          <num>        <num>
#>  1:  1.3581001 6.549161e-10
#>  2: -0.5739922 3.881849e-08
#>  3:  0.3609620 6.758544e-01
#>  4:  0.3443472 6.758544e-01
#>  5:  0.3780814 6.758544e-01
#>  6:  0.1679153 7.838229e-01
#>  7:  0.2817762 8.930127e-01
#>  8:  0.2808732 8.930127e-01
#>  9:  0.2764745 9.891226e-01
#> 10:  0.2171718 9.891226e-01

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Contributions

Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.

Citation

To cite unihtee and the papers introducing the underlying framework, use the following BibTeX entries:

@manual{unihtee,
  title = {unihtee: Univariate Heterogeneous Treatment Effect Estimation},
  author = {Philippe Boileau},
  note = {R package version 0.0.1}
}

@article{boileau2025,
  title = {A Nonparametric Framework for Treatment Effect Modifier Discovery in High Dimensions},
  author = {Boileau, Philippe and Leng, Ning and Hejazi, Nima S and {van der Laan}, Mark and Dudoit, Sandrine},
  year = {2025},
  journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume = {87},
  number = {1},
  pages = {157--185},
  issn = {1369-7412},
  doi = {10.1093/jrsssb/qkae084}
}

@article{boileau2022,
  author = {Boileau, Philippe and Qi, Nina Ting and van der Laan, Mark J and Dudoit, Sandrine and Leng, Ning},
  title = {A flexible approach for predictive biomarker discovery},
  journal = {Biostatistics},
  year = {2022},
  month = {07},
  issn = {1465-4644},
  doi = {10.1093/biostatistics/kxac029},
  url = {https://doi.org/10.1093/biostatistics/kxac029}
}

License

The contents of this repository are distributed under the Apache 2.0 license. See the LICENSE.md and LICENSE files for details.

References

Boileau, Philippe, Ning Leng, Nima S Hejazi, Mark van der Laan, and Sandrine Dudoit. 2025. “A Nonparametric Framework for Treatment Effect Modifier Discovery in High Dimensions.” Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1093/jrsssb/qkae084.

Boileau, Philippe, Nina Ting Qi, Mark J van der Laan, Sandrine Dudoit, and Ning Leng. 2022. “A flexible approach for predictive biomarker discovery.” Biostatistics, July. https://doi.org/10.1093/biostatistics/kxac029.