Getting Started

In CausalQueries: Make, Update, and Query Binary Causal Models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

if(!requireNamespace("fabricatr", quietly = TRUE)) {
  install.packages("fabricatr")
}

library(CausalQueries)
library(fabricatr)
library(knitr)

Make a model

Generating: To make a model you need to provide a DAG statement to make_model.
For instance

• "X->Y"
• "X -> M -> Y <- X" or
• "Z -> X -> Y <-> X".

# examples of models
xy_model <- make_model("X -> Y")
iv_model <- make_model("Z -> X -> Y <-> X")

Graphing: Once you have made a model you can inspect the DAG:

plot(iv_model)

Inspecting: The model has a set of parameters and a default distribution over these.

xy_model |> grab("parameters_df") |> kable()

Tailoring: These features can be edited using set_restrictions, set_priors and set_parameters. Here is an example of setting a monotonicity restriction (see ?set_restrictions for more):

Here is an example of setting a monotonicity restriction (see ?set_restrictions for more):

iv_model <- 
  iv_model |> set_restrictions(decreasing('Z', 'X'))

Here is an example of setting priors (see ?set_priors for more):

iv_model <- 
  iv_model |> set_priors(distribution = "jeffreys")

Simulation: Data can be drawn from a model like this:

data <- make_data(iv_model, n = 4) 

data |> kable()

Model updating

Updating: Update using update_model. You can pass all rstan arguments to update_model.

df <- fabricatr::fabricate(N = 100, X = rbinom(N, 1, .5), Y = rbinom(N, 1, .25 + X*.5))

xy_model <- 
  xy_model |> 
  update_model(df, refresh = 0)

Inspecting: You can access the posterior distribution on model parameters directly thus:

xy_model |> grab("posterior_distribution") |> 
  head() |> kable()

where each row is a draw of parameters.

Query model

Querying: You ask arbitrary causal queries of the model.

Examples of unconditional queries:

xy_model |> 
  query_model("Y[X=1] > Y[X=0]", using = c("priors", "posteriors")) |>
  kable()

Examples of conditional queries:

xy_model |> 
  query_model("Y[X=1] > Y[X=0]", using = c("priors", "posteriors"),
              given = "X==1 & Y == 1") |>
  kable()

Queries can even be conditional on counterfactual quantities. Here the probability of a positive effect given some effect:

xy_model |> 
  query_model("Y[X=1] > Y[X=0]", using = c("priors", "posteriors"),
              given = "Y[X=1] != Y[X=0]") |>
  kable()

CausalQueries documentation built on June 22, 2024, 6:50 p.m.