Methods for Bayesian causal forest fits

A fit from bcf() is a <bartisan_fit> with a treatment named, so it takes every method that fit takes. print() adds the treatment and says where the effect comes from, and plot() draws the conditional effects.

Usage

# S3 method for class 'bcf_fit'
print(x, digits = 3L, ...)

# S3 method for class 'bcf_fit'
plot(x, level = 0.95, comparison = "difference", marginal = TRUE, ...)

Arguments

x

a <bcf_fit> object; the output of a call to bcf().

digits

integer; the number of significant digits to print.

...

for plot(), further arguments passed to estimate_effect(); otherwise ignored.

level

numeric; the level of the credible interval. Default is .95.

comparison

passed to estimate_effect(), which computes the numbers.

marginal

logical; whether to draw the marginal effect beside the conditional ones. Default is TRUE.

Value

plot() returns a ggplot2 object. print() returns its input invisibly.

Details

The effect itself is estimate_effect()'s to report, and summary() on a <bcf_fit> is the same summary of the forests it is on any other fit, with a line at the end saying so. That way the same call prints the same thing whether the model came from bcf() or from bartisan() with a vc() term.

See also

estimate_effect() for the effect and its estimands; bcf() for the model

Examples

data("rhc")
set.seed(123)

# Note: we use small sampler settings that would not be
# appropriate for analysis; this is just for the
# examples
fit <- bcf(death ~ age + sex + meanbp + aps,
           treat = ~ rhc, data = rhc, num_trees = 10,
           num_burn = 50, num_draws = 50,
           propensity_args = list(num_trees = 10,
                                  num_burn = 50,
                                  num_draws = 50),
           verbose = FALSE)
#> ℹ Using `family = binomial()`.
#> ℹ Set `family` explicitly to silence this message.

fit
#> Generalized BART
#> 
#> Call:
#> bcf(formula = death ~ age + sex + meanbp + aps, treat = ~rhc, 
#>     data = rhc, propensity_args = list(num_trees = 10, num_burn = 50, 
#>         num_draws = 50), num_trees = 10, num_burn = 50, num_draws = 50, 
#>     verbose = FALSE)
#> 
#> Family: "binomial" with the "logit" link
#> Observations: 1500
#> Structure: 2 forests of 10 trees, soft decision rules
#> Draws: 50 kept after 50 warmup
#> 
#> Posterior means: b.rhc.0 = 0.597, b.rhc.1 = 0.729
#> 
#> Treatment: "rhc"
#> Effect moderators: "age", "sex", "meanbp", and "aps"
#> ℹ `estimate_effect()` reports the treatment effect, with the average potential
#>   outcomes beside it; `plot()` draws the conditional ones.

# The effect, with the potential outcomes it is a
# difference of
estimate_effect(fit)
#> Average treatment effect (difference)
#> 
#> Treatment: `rhc`
#> Averaged over 1500 units
#> 
#>     contrast estimate   lower  upper    n
#>  Y[1] - Y[0]   0.0159 -0.0126 0.0419 1500
#> 
#> Average potential outcomes
#> 
#>  quantity estimate lower upper
#>      Y[0]    0.649 0.627 0.679
#>      Y[1]    0.665 0.638 0.688
#> 
#> ℹ estimate is the posterior mean; lower and upper bound the 95% equal-tailed
#>   credible interval.
#> ℹ Y[a] is the average response with `rhc` set to "a".

# The conditional effects, ordered, with the marginal
# effect beside them
plot(fit)