# Using the tidytreatment package with bartCause

#### Joshua J Bon

#### 2025-12-06

This vignette demonstrates an example workflow for heterogeneous treatment effect models using the `BART` package for fitting Bayesian Additive Regression Trees and `tidytreatment` for investigating the output of such models. The `tidytreatment` package can also be used with `bartMachine` models, support for `bcf` is coming soon (see branch `bcf-hold` on github).

## Simulate data

Below we load packages and simulate data using the scheme described by J. Hill and Su (2013) with the additional of 1 categorical variable. It it implemented in the function `simulate_hill_su_data()`:

    # load packages
    library(bartCause)
    library(stan4bart)
    library(tidytreatment)
    library(dplyr)
    library(tidybayes)
    library(ggplot2)

    # set seed so vignette is reproducible
    set.seed(101)

    # simulate data
    sim <- simulate_su_hill_data(n = 100, treatment_linear = FALSE,  omega = 0, add_categorical = TRUE,
                                 n_subjects = 10, sd_subjects = 0.1,
                                 coef_categorical_treatment = c(0,0,1),
                                 coef_categorical_nontreatment = c(-1,0,-1)
    )

Now we can take a look at some data summaries.

    # non-treated vs treated counts:
    table(sim$data$z)
    #>
    #>  0  1
    #> 48 52

    dat <- sim$data
    # a selection of data
    dat %>% select(y, z, c1, x1:x3) %>% head()
    #>            y z c1         x1          x2         x3
    #> 1  7.4280183 1  3  0.1820194 -0.06353599  0.3524953
    #> 2  0.5275992 0  1 -2.3814015  0.20423903  0.8935155
    #> 3  3.5616838 0  2 -0.5518608 -1.10040685  1.9034043
    #> 4  9.3384301 1  2  0.7267062 -0.11821280 -0.5202530
    #> 5  3.7328197 1  1 -1.8133403  0.73768934  0.2913854
    #> 6 -1.7501469 0  2 -0.8688956 -0.38677915 -0.7522927

    # repeated observation counts for subjects:
    table(sim$data$subject_id)
    #>
    #>  1  2  3  4  5  6  7  8  9 10
    #>  8 10  6 14 14 10  9  8 13  8

## Fit the regression model

Run the model to be used to assess treatment effects. Here we will use the `bartCause`, for causal inference with Bayesian additive regression trees (BART) (J. L. Hill 2011). For more on BART see Chipman, George, and E. McCulloch (2010) and Sparapani et al. (2016). The package can be found on CRAN.

We are following the procedure in Hahn, Murray, and Carvalho (2020) (albeit without their causal forest model) where we estimate a propensity score for being assigned to the treatment regime, which improves estimation properties. This is done automatically in `bartCause`. The procedure is roughly as follows:

1. Fit ‘variable selection’ model (VS): Regress response variable against all potential confounders (i.e. no treatment variable)
2. Select a subset of confounders from the VS model which are most associated with the response variable
3. Fit a ‘propensity score’ model (PS): A binary response model estimating the treatment assignment propensity score for using only the variables selected in step 2
4. Fit the treatment effect model (TE): Using the all potential confounders and propensity score from step 3

Note: Using the automatic treatment assignment model in `bartCause` ignores step 1-2 uses all potential confounders to estimate the PS model.

    # STEP 1 VS Model: Regress y ~ covariates
    vs_bart <- stan4bart(y ~ bart(. - subject_id - z) + (1|subject_id),
                                 data = dat, iter = 5000, verbose = -1)

    # STEP 2: Variable selection
      # select most important vars from y ~ covariates model
      # Note: This is an overly simple selection mechanism.
      # See package {bartMan} vignettes and https://doi.org/10.52933/jdssv.v4i1.79
      # for discussion of variable importance methods.
    covar_ranking <- covariate_importance(vs_bart)
    var_select <- covar_ranking %>%
      filter(avg_inclusion > mean(avg_inclusion) - sd(avg_inclusion)) %>% # at minimum: within 1 sd of mean inclusion
      pull(variable)

    # change categorical variables to just one variable
    var_select <- unique(gsub("c1.[1-3]$","c1", var_select))

    var_select
    #>  [1] "c1"  "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"
    # includes all covariates

    # STEP 3 PS Model: Regress z ~ selected covariates
    ps_bart <- stan4bart(z ~ bart(. - subject_id - y) + (1|subject_id),
                                 data = dat, iter = 5000, verbose = -1)

    # store propensity score in data
    prop_score <- fitted(ps_bart)

    # Step 4 TE Model: Regress y ~ z + covariates + propensity score
    te_bart <- bartc(response = y, treatment = z, confounders = x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10,
                     parametric = (1|subject_id), data = dat, method.trt = prop_score,
                     iter = 5000, bart_args = list(keepTrees = TRUE))
    #> fitting response model via method 'bart'

    #* The posterior samples are kept small to manage size on CRAN

## Extract the posterior (tidy style)

Methods for extracting the posterior in a tidy format is included in the `tidytreatment`.

    # get model parameters (excluding BART paramaters)
    posterior_params <- tidy_draws(te_bart)

    posterior_fitted <- epred_draws(te_bart, value = "fitted")
    #> Warning in extract.stan4bartFit(object = object, type = "ev", combine_chains =
    #> FALSE, : unused arguments ignored

    # Function to tidy predicted draws (adds predicted noise to fitted values)
    posterior_pred <- predicted_draws(te_bart, value = "predicted")

## Use some plotting functions from the `tidybayes` package

Since `tidytreatment` follows the `tidybayes` output specifications, functions from `tidybayes` should work.

    treatment_var_and_c1 <-
      dat %>%
      select(z,c1) %>%
      mutate(.row = 1:n(), z = as.factor(z))

    posterior_fitted %>%
      left_join(treatment_var_and_c1, by = ".row") %>%
      ggplot() +
      stat_halfeye(aes(x = z, y = fitted)) +
      facet_wrap(~c1, labeller = as_labeller( function(x) paste("c1 =",x) ) ) +
      xlab("Treatment (z)") + ylab("Posterior predicted value") +
      theme_bw() + ggtitle("Effect of treatment with 'c1' on posterior fitted values")

## Calculate Treatment Effects

Posterior conditional (average) treatment effects can be calculated using the `treatment_effects` function. This function finds the posterior values of \[ \tau(x) = \text{E}(y ~ \vert~ T = 1, X = x) - \text{E}(y ~ \vert~ T = 0, X = x) \] for each unit of measurement, \(i\), (e.g. subject) in the data sample.

Some histogram summaries are presented below.

    # sample based (using data from fit) conditional treatment effects, posterior draws
    posterior_treat_eff <- treatment_effects(te_bart)

    # check lines up with summary results...

    # Histogram of treatment effect (all draws)
    posterior_treat_eff %>%
      ggplot() +
      geom_histogram(aes(x = icate), binwidth = 0.1, colour = "white") +
      theme_bw() + ggtitle("Histogram of treatment effect (all draws)")

    # Histogram of treatment effect (median for each subject)
    posterior_treat_eff %>% summarise(cte_hat = median(icate)) %>%
      ggplot() +
      geom_histogram(aes(x = cte_hat), binwidth = 0.1, colour = "white") +
      theme_bw() + ggtitle("Histogram of treatment effect (median for each subject)")

## References

Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. “BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98. https://doi.org/10.1214/09-AOAS285.
Hahn, P. Richard, Jared S. Murray, and Carlos M. Carvalho. 2020. “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion).” Bayesian Analysis 15 (3): 965–1056. https://doi.org/10.1214/19-BA1195.
Hill, Jennifer L. 2011. “Bayesian Nonparametric Modeling for Causal Inference.” Journal of Computational and Graphical Statistics 20 (1): 217–40. https://doi.org/10.1198/jcgs.2010.08162.
Hill, Jennifer, and Yu-Sung Su. 2013. “Assessing Lack of Common Support in Causal Inference Using Bayesian Nonparametrics: Implications for Evaluating the Effect of Breastfeeding on Children’s Cognitive Outcomes.” The Annals of Applied Statistics 7 (3): 1386–1420. https://doi.org/10.1214/13-AOAS630.
Sparapani, Rodney A, Brent R Logan, Robert E McCulloch, and Purushottam W Laud. 2016. “Nonparametric Survival Analysis Using Bayesian Additive Regression Trees (BART).” Statistics in Medicine 35 (16): 2741–53. https://doi.org/10.1002/sim.6893.