query_model: Generate estimands dataframe

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

Generate estimands dataframe

Description

Calculated from a parameter vector, from a prior or from a posterior distribution.

Usage

query_model(
  model,
  queries = NULL,
  given = NULL,
  using = list("parameters"),
  parameters = NULL,
  stats = NULL,
  n_draws = 4000,
  expand_grid = FALSE,
  case_level = FALSE,
  query = NULL,
  cred = 95
)

Arguments

model	A causal_model. A model object generated by make_model.
queries	A vector of strings or list of strings specifying queries on potential outcomes such as "Y[X=1] - Y[X=0]".
given	A vector or list of strings specifying givens. A given is a quoted expression that evaluates to a logical statement. Allows estimand to be conditioned on *observational* (or counterfactual) distribution.
using	A vector or list of strings. Whether to use priors, posteriors or parameters.
parameters	A vector of real numbers in [0,1]. Values of parameters to specify (optional). By default, parameters is drawn from model$parameters_df.
stats	Functions to be applied to estimand distribution. If NULL, defaults to mean, standard deviation, and 95% confidence interval. Functions should return a single numeric value.
n_draws	An integer. Number of draws.
expand_grid	Logical. If TRUE then all combinations of provided lists are examined. If not then each list is cycled through separately. Defaults to FALSE.
case_level	Logical. If TRUE estimates the probability of the query for a case.
query	alias for queries
cred	size of the credible interval ranging between 0 and 100

Details

Queries can condition on observed or counterfactual quantities. Nested or "complex" counterfactual queries of the form Y[X=1, M[X=0]] are allowed.

Value

A DataFrame with columns Model, Query, Given and Using defined by corresponding input values. Further columns are generated as specified in stats.

Examples

model <- make_model("X -> Y")
query_model(model, "Y[X=1] - Y[X = 0]", using = "priors")
query_model(model, "Y[X=1] > Y[X = 0]", using = "parameters")
query_model(model, "Y[X=1] > Y[X = 0]", using = c("priors", "parameters"))


# `expand_grid= TRUE` requests the Cartesian product of arguments

models <- list(
 M1 = make_model("X -> Y"),
 M2 = make_model("X -> Y") |>
   set_restrictions("Y[X=1] < Y[X=0]")
 )


query_model(
  models,
  query = list(ATE = "Y[X=1] - Y[X=0]",
               Share_positive = "Y[X=1] > Y[X=0]"),
  given = c(TRUE,  "Y==1 & X==1"),
  using = c("parameters", "priors"),
  expand_grid = FALSE)

query_model(
  models,
  query = list(ATE = "Y[X=1] - Y[X=0]",
               Share_positive = "Y[X=1] > Y[X=0]"),
  given = c(TRUE,  "Y==1 & X==1"),
  using = c("parameters", "priors"),
  expand_grid = TRUE)

# An example of a custom statistic: uncertainty of token causation
f <- function(x) mean(x)*(1-mean(x))

query_model(
  model,
  using = list( "parameters", "priors"),
  query = "Y[X=1] > Y[X=0]",
  stats = c(mean = mean, sd = sd, token_variance = f))

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