---
title: "Using the tidytreatment package with BART (binary outcome)"
author: "Joshua J Bon"
date: "`r Sys.Date()`"
bibliography: vignette.bib
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using the tidytreatment package with BART (binary outcome)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.dim = c(6, 4)
)

suppressPackageStartupMessages({
    library(BART)
    library(tidytreatment)
    library(dplyr)
    library(tidybayes)
    library(ggplot2)
  })
  
  # load pre-computed data and model
  sim <- suhillsim1
  te_model <- bartmodel1
  
  # pre compute
  posterior_treat_eff <- treatment_effects(te_model, treatment = "z", newdata = sim$data) 
  posterior_treat_eff_on_treated <- treatment_effects(te_model, treatment = "z", newdata = sim$dat, subset = "treated") 
  
```

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 @Hill2013 with the additional of 1 categorical variable. It it implemented in the function `simulate_hill_su_data()`:

```{r load-data-print, echo = TRUE, eval = FALSE}

# load packages
library(BART)
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,
                             coef_categorical_treatment = c(0,0,1),
                             coef_categorical_nontreatment = c(-1,0,-1)
)

# binary in this example

sim$data$y <- as.integer(sim$data$y > 2.7)
  
```

Now we can take a look at some data summaries.

```{r data-summary, echo = TRUE, eval = TRUE}

# non-treated vs treated counts:
table(sim$data$z)

dat <- sim$data
# a selection of data
dat %>% select(y, z, c1, x1:x3) %>% head()

```

## Fit the regression model

Run the model to be used to assess treatment effects. Here we will use `BART`, which is one implementation of Bayesian Additive Regression Trees in `R` [@Chipman2010; @sparapani2016]. The package can be found on [CRAN](https://cran.r-project.org/package=BART).

We are following the procedure in @Hahn2020 (albeit without their more sophisticated model) where we estimate a propensity score for being assigned to the treatment regime, which improves estimation properties. The procedure is roughly as follows:

1. Fit 'variable selection' model (VS): Regress outcome against covariates (excluding treatment variable)
2. Select a subset of covariate from the VS model which are most associated with the outcome
3. Fit a 'propensity score' model (PS): A probit/logit model estimating the propensity score using only the covariates selected in step 2
4. Fit the treatment effect model (TE): Using the original covariates and propensity score from step 3

```{r run-bart, echo = TRUE, eval = FALSE}
  
# STEP 1 VS Model: Regress y ~ covariates
var_select_bart <- pbart(x.train = select(dat,-y,-z), 
                         y.train = pull(dat, y),
                         sparse = TRUE,
                         nskip = 2000, 
                         ndpost = 5000)

# 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(var_select_bart)
var_select <- covar_ranking %>% 
  filter(avg_inclusion >= quantile(avg_inclusion, 0.5)) %>%
  pull(variable)

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

var_select

# STEP 3 PS Model: Regress z ~ selected covariates
  # BART::pbart is for probit regression 
prop_bart <- pbart(
  x.train = select(dat, all_of(var_select)), 
  y.train = pull(dat, z), 
  nskip = 2000,
  ndpost = 5000
) 

# store propensity score in data
dat$prop_score <-  prop_bart$prob.train.mean

# Step 4 TE Model: Regress y ~ z + covariates + propensity score
te_model <- pbart(
  x.train = select(dat,-y),
  y.train = pull(dat, y),
  nskip = 10000L,
  ndpost = 200L, #*
  keepevery = 100L #*
)

#* 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`.

```{r tidy-bart-fit, echo=TRUE, cache=FALSE}

posterior_fitted <- epred_draws(te_model, value = "fit", scale = "prob", include_newdata = FALSE)
# include_newdata = FALSE, avoids returning the newdata with the fitted values
# as it is so large. newdata argument must be specified for this option in BART models. 
# The `.row` variable makes sure we know which row in the newdata the fitted
# value came from (if we dont include the data in the result).

posterior_fitted

```

```{r tidy-bart-pred, eval=FALSE, echo=TRUE, cache=FALSE}

# Function to tidy predicted draws also, this adds random normal noise by default
posterior_pred <- predicted_draws(te_model, include_newdata = FALSE)

```

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

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

```{r plot-tidy-bart, echo=TRUE, cache=FALSE}

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 = fit)) + 
  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.

```{r post-treatment, eval = FALSE}

# sample based (using data from fit) conditional treatment effects, posterior draws
posterior_treat_eff <- 
  treatment_effects(te_model, treatment = "z", scale = "prob", newdata = dat) 

```
```{r cates-hist, echo=TRUE, cache=FALSE}

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

```
```{r att-ate, eval=FALSE}
# get the ATE and ATT directly:

posterior_ate <- tidy_ate(te_model, treatment = "z", scale = "prob", newdata = dat)
posterior_att <- tidy_att(te_model, treatment = "z", scale = "prob", newdata = dat)

```

```{r ate-trace-setup, eval = TRUE, echo = FALSE}

posterior_ate <- posterior_treat_eff %>% group_by(.chain, .iteration, .draw) %>%
  summarise(ate = mean(cte), .groups = "drop")

```

We can create a trace plot for the treatment effect summaries easily too:

```{r ate-trace, eval=TRUE, echo=TRUE}

posterior_ate %>% ggplot(aes(x = .draw, y = ate)) +
  geom_line() +
  theme_bw() + 
  ggtitle("Trace plot of ATE")

```

We can also focus on the treatment effects for just those that are treated. 

```{r post-te-treated, echo=TRUE, eval=FALSE}

# sample based (using data from fit) conditional treatment effects, posterior draws
posterior_treat_eff_on_treated <- 
  treatment_effects(te_model, treatment = "z", newdata = dat, subset = "treated") 

```

```{r cates-hist-treated, echo=TRUE, cache=FALSE}

posterior_treat_eff_on_treated %>% 
  ggplot() +
  geom_histogram(aes(x = cte), binwidth = 0.1, colour = "white") + 
  theme_bw() + ggtitle("Histogram of treatment effect (all draws from treated subjects)")

```

Plots can be made that stack each subjects posterior CIs of the CATEs. 

```{r cates-stack-plot, echo=TRUE, cache=FALSE}

posterior_treat_eff %>% select(-z) %>% point_interval() %>%
  arrange(cte) %>% mutate(.orow = 1:n()) %>% 
  ggplot() + 
  geom_interval(aes(x = .orow, y= cte, ymin = .lower, ymax = .upper)) +
  geom_point(aes(x = .orow, y = cte), shape = "circle open", alpha = 0.5) + 
  ylab("Median posterior CATE for each subject (95% CI)") +
  theme_bw() + coord_flip() + scale_colour_brewer() +
  theme(axis.title.y = element_blank(), 
        axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(),
        legend.position = "none")

```

We can also plot the CATEs varying over particular covariates. In this example, instead of grouping by subject, we group by the variable of interest, and calculate the posterior summaries over this variable.

```{r cates-line-plot, echo=TRUE, cache=FALSE}

posterior_treat_eff %>%
  left_join(tibble(c1 = dat$c1, .row = 1:length(dat$c1) ), by = ".row") %>%
  group_by(c1) %>%
  ggplot() + 
  stat_halfeye(aes(x = c1, y = cte), alpha = 0.7) +
  scale_fill_brewer() +
  theme_bw() + ggtitle("Treatment effect by `c1`")


```

## Common support calculations

Common support testing [@Hill2013] can be tested directly, or a Boolean can be included when calculating the treatment effects.

```{r common-support, echo=TRUE, results='hide', cache=FALSE}

# NOTE: common support calculated on linear scale (not probability scale for probit/logit)

# calculate common support directly
# argument 'modeldata' must be specified for BART models 
csupp_chisq <- has_common_support(te_model, treatment = "z", modeldata = dat,
                             method = "chisq", cutoff = 0.05)

csupp_chisq %>% filter(!common_support)

csupp_sd <- has_common_support(te_model, treatment = "z", modeldata = dat,
                             method = "sd")
csupp_sd %>% filter(!common_support)

# calculate treatment effects (on those who were treated) 
# and include only those estimates with common support
posterior_treat_eff_on_treated <- 
  treatment_effects(te_model, treatment = "z", subset = "treated", newdata = dat,
                    common_support_method = "sd") 

```

## Investigating variable importance

We can count how many times a variable was included in the BART (on average) in conjunction with the treatment effect, or overall. This method uses a simple average of occurrences, see @bleich2014variable for more sophisticated methods.

```{r interaction-investigator, echo=TRUE, cache=FALSE}

  treatment_interactions <-
    covariate_with_treatment_importance(te_model, treatment = "z")

  treatment_interactions %>% 
    ggplot() + 
    geom_bar(aes(x = variable, y = avg_inclusion), stat = "identity") +
    theme_bw() + ggtitle("Important variables interacting with treatment ('z')") + ylab("Inclusion counts") +
  theme(axis.text.x = element_text(angle = 45, hjust=1))
  
  variable_importance <-
    covariate_importance(te_model)

  variable_importance %>% 
    ggplot() + 
    geom_bar(aes(x = variable, y = avg_inclusion), stat = "identity") +
    theme_bw() + ggtitle("Important variables overall") +
    ylab("Inclusion counts") +
    theme(axis.text.x = element_text(angle = 45, hjust=1))
  
```

## Model checking and convergence

Here are some examples of model checking we can do.

Code for trace plot of model variance ($\sigma^2$).

```{r sigma-trace, echo=TRUE, cache=FALSE}

# includes skipped MCMC samples
variance_draws(te_model, value = "siqsq") %>% 
  filter(.draw > 10000) %>%
  ggplot(aes(x = .draw, y = siqsq)) +
  geom_line() +
  theme_bw() + 
  ggtitle("Trace plot of model variance post warm-up")

```
Code for examining model residuals.

```{r convergence-bart, echo=TRUE, cache=FALSE}

res <- residual_draws(te_model, response = pull(dat, y), include_newdata = FALSE)
res %>%   
  point_interval(.residual, y, .width = c(0.95) ) %>%
  select(-y.lower, -y.upper) %>%
  ggplot() + 
  geom_pointinterval(aes(x = y, y = .residual, ymin = .residual.lower,  ymax = .residual.upper), alpha = 0.2) +
  scale_fill_brewer() +
  theme_bw() + ggtitle("Residuals vs observations")

res %>% summarise(.fitted = mean(.fitted), y = first(y)) %>% 
  ggplot(aes(x = y, y = .fitted)) +
  geom_point() + 
  geom_smooth(method = "lm") + 
  theme_bw() + ggtitle("Observations vs fitted")

res %>% summarise(.residual = mean(.residual)) %>%
  ggplot(aes(sample = .residual)) + 
  geom_qq() + 
  geom_qq_line() + 
  theme_bw() + ggtitle("Q-Q plot of residuals")

```

## References
