declare_potential_outcomes.R

#' Potential Outcomes
#'
#' @inheritParams declare_internal_inherit_params
#'
#' @return a function that returns a data.frame
#'
#' @export
#'
#' @details
#'
#' A `declare_potential_outcomes` declaration returns a function. That function takes data and returns data with potential outcomes columns appended. These columns describe the outcomes that each unit would express if that unit were in the corresponding treatment condition.
#'
#' The potential outcomes function can sometimes be a stumbling block for users, as some are uncomfortable asserting anything in particular about the very causal process that they are conducting a study to learn about! We recommend trying to imagine what your preferred theory would predict, what an alternative theory would predict, and what your study would reveal if there were no differences in potential outcomes for any unit (i.e., all treatment effects are zero).
#'
#' @examples
#' 
#' # Default handler
#'
#' # By default, there are two ways of declaring potential outcomes:
#' # as separate variables or using a formula:
#'
#' # As separate variables
#'
#' my_potential_outcomes <- declare_potential_outcomes(
#'   Y_Z_0 = .05,
#'   Y_Z_1 = .30 + .01 * age
#' )
#'
#' # Using a formula
#'  my_potential_outcomes <- declare_potential_outcomes(Y ~ .05 + .25 * Z + .01 * age * Z)
#'
#'  # conditions defines the "range" of the potential outcomes function
#'  my_potential_outcomes <- declare_potential_outcomes(
#'    formula = Y ~ .05 + .25 * Z + .01 * age * Z,
#'    conditions = 1:4
#'  )
#'
#'  # Multiple assignment variables can be specified in conditions.
#'  # A 2x2 factorial potential outcome:
#'
#'  my_potential_outcomes <- declare_potential_outcomes(
#'    formula = Y ~ .05 + .25 * Z1 + .01 * age * Z2,
#'    conditions = list(Z1 = 0:1, Z2 = 0:1)
#'  )
#'
#' ########################################################
#' # Custom handler
#'
#' my_po_function <- function(data) {
#'
#'   data$Y_treated   <- rexp(nrow(data), .2)
#'   data$Y_untreated <- rexp(nrow(data), .4)
#'   data
#' }
#'
#' custom_potential <- declare_potential_outcomes(handler = my_po_function)
#'
declare_potential_outcomes <- make_declarations(potential_outcomes_handler, "potential_outcomes");


### Default handler calls either the formula handler or non-formula handler
### this can be determind at declare time in the validation_fn, and the correct function returned instead
### If possible, we do so, even though much of the logic is essentially duplicated
### this makes tracing the execution in run_design much simpler

potential_outcomes_handler <-  function(..., data, level) {
  (function(formula, ...) UseMethod("potential_outcomes"))(..., data = data, level = level)
}

validation_fn(potential_outcomes_handler) <-  function(ret, dots, label) {
  
  declare_time_error_if_data(ret)
  
  # Below is a similar redispatch strategy, only at declare time
  validation_delegate <- function(formula=NULL, ...) {
    potential_outcomes <- function(formula, ...) UseMethod("potential_outcomes", formula)
    for (c in class(formula)) {
      s3method <- getS3method("potential_outcomes", class(formula))
      if (is.function(s3method)) return(s3method)
    }
    declare_time_error("Could not find appropriate implementation", ret)
  }
  
  s3method <- eval_tidy(quo(validation_delegate(!!!dots)))
  
  # explicitly name all dots, for easier s3 handler validation
  dots <- rename_dots(s3method, dots)
  
  if ("level" %in% names(dots)) {
    dots$level <- reveal_nse_helper(dots$level)
  }
  
  
  ret <- build_step(
    currydata(s3method, dots, strictDataParam = attr(ret, "strictDataParam")),
    handler = s3method,
    dots = dots,
    label = label,
    step_type = attr(ret, "step_type"),
    causal_type = attr(ret, "causal_type"),
    call = attr(ret, "call"))
  
  if (has_validation_fn(s3method)) ret <- validate(s3method, ret, dots, label)
  
  ret
}

#' @param formula a formula to calculate potential outcomes as functions of assignment variables
#' @param conditions see \code{\link{expand_conditions}}
#' @param assignment_variables The name of the assignment variable
#' @param level a character specifying a level of hierarchy for fabricate to calculate at
#' @param data a data.frame
#' @importFrom fabricatr fabricate
#' @importFrom rlang quos := !! !!! as_quosure
#' @rdname declare_potential_outcomes
potential_outcomes.formula <- function(formula,
                                       conditions = c(0, 1),
                                       assignment_variables = "Z", # only used to provide a default - read from names of conditions immediately after.
                                       data,
                                       level = NULL,
                                       label = outcome_variable) {
  
  outcome_variable <- as.character(formula[[2]])
  
  to_restore <- assignment_variables %i% colnames(data)
  to_null <- setdiff(assignment_variables, to_restore)
  
  # Build a single large fabricate call -
  # fabricate( Z=1, Y_Z_1=f(Z), Z=2, Y_Z_2=f(Z), ..., Z=NULL)
  condition_quos <- quos()
  
  ### If assn vars already present, swap them out
  if (length(to_restore) > 0) {
    restore_mangled <- paste(rep("_", max(nchar(colnames(data)))), collapse = "")
    
    restore_mangled <- setNames(
      lapply(to_restore, as.symbol),
      paste0(".", restore_mangled, to_restore)
    )
    
    condition_quos <- c(condition_quos, quos(!!!restore_mangled))
    
  }
  
  # build call
  expr = as_quosure(formula)
  for (i in 1:nrow(conditions)) {
    
    condition_values <- conditions[i, , drop = FALSE]
    out_name <- paste0(outcome_variable, "_", paste0(assignment_variables, "_", condition_values, collapse = "_"))
    
    condition_quos <- c(condition_quos, quos(!!!condition_values, !!out_name := !!expr) )
  }
  
  # clean up
  if (length(to_restore) > 0) {
    to_restore <-  setNames(
      lapply(names(restore_mangled), as.symbol),
      to_restore
    )
    restore_mangled <- lapply(restore_mangled, function(x) NULL)
    condition_quos <- c(condition_quos, quos(!!!to_restore), quos(!!!restore_mangled))
  }
  
  if (length(to_null) > 0) {
    to_null <-  lapply(setNames(nm = to_null), function(x) NULL)
    condition_quos <- c(condition_quos, quos(!!!to_null))
  }
  
  
  if (is.character(level)) {
    condition_quos <- quos(!!level := modify_level(!!!condition_quos))
  }
  
  ### Actually do it and return
  ### Note ID_label=NA
  structure(
    fabricate(data = data,!!!condition_quos, ID_label = NA),
    outcome_variable = outcome_variable,
    assignment_variables = assignment_variables)
  
}


validation_fn(potential_outcomes.formula) <- function(ret, dots, label) {
  
  dots$formula <- eval_tidy(dots$formula)
  outcome_variable <- as.character(dots$formula[[2]])

  if (length(dots$formula) < 3) {
    declare_time_error("Must provide an outcome in potential outcomes formula", ret)
  }
  
  if ("ID_label" %in% names(dots)) {
    declare_time_error("Must not pass ID_label.", ret)
  }
  
  if ("assignment_variables" %in% names(dots)) {
    dots$assignment_variables <- reveal_nse_helper(dots$assignment_variables)
  }
  
  dots$conditions <- eval_tidy(quo(expand_conditions(!!!dots)))
  dots$assignment_variables <- names(dots$conditions)
  
  ret <- build_step(currydata(potential_outcomes.formula,
                              dots,
                              strictDataParam = attr(ret, "strictDataParam"),
                              cloneDots = FALSE
  ),
  handler = potential_outcomes.formula,
  dots = dots,
  label = label,
  step_type = attr(ret, "step_type"),
  causal_type = attr(ret, "causal_type"),
  call = attr(ret, "call"))
  
  
  # Note that this sets a design_validation callback for later use!!! see below
  # step_meta is the data that design_validation will use for design time checks
  structure(ret,
            potential_outcomes_formula = formula,
            step_meta = list(outcome_variables = outcome_variable,
                             assignment_variables = names(dots$conditions)),
            design_validation = pofdv)
}


# A design time validation
#
#  Checks for unrevealed outcome variables.
#
#  If there are any, inject a declare_reveal step after the latest assign/reveal of an assn variable
#
#
pofdv <- function(design, i, step){
  
  if (i == length(design)) {
    return(design)
  }
  
  this_step_meta <- attr(step, "step_meta")
  
  check <- function(var_type, step_type, step_attr, callback = identity, from = 1, to = length(design)) {
    vars <- this_step_meta[[var_type]]
    
    assn_steps <- Filter(function(step_j) attr(step_j, "step_type") == step_type,
                         design[from:to])
    
    for (step_j in assn_steps) {
      if (is.null(step_meta <- attr(step_j, "step_meta"))) next;
      step_assn <- step_meta[[step_attr]]
      vars <- setdiff(vars, step_assn)
      if (length(vars) == 0) return(c())
    }
    
    callback(vars)
  }
  
  unrevealed_outcomes <- check("outcome_variables", "reveal", "outcome_variables",
                               from = i + 1,
                               function(vars){
                                 vars
                               }
  )
  
  if (length(unrevealed_outcomes) == 0) return(design)
  
  # warning(
  #   "Outcome variables (", paste(unrevealed_outcomes, sep = ", "),
  #   ") were declared in a potential outcomes step (", attr(step, "label"),
  #   "), but never later revealed.", call. = FALSE)
  
  prev_unassigned <- check("assignment_variables",  "assignment", "assignment_variables", to = i - 1)
  prev_unrevealed <- check("assignment_variables", "reveal", "outcome_variables", to = i - 1)
  
  if (length(prev_unassigned %i% prev_unrevealed) == 0) {
    
    new_step <- eval_tidy(quo(declare_reveal(outcome_variables = !!this_step_meta$outcome_variables,
                                             assignment_variables = !!this_step_meta$assignment_variables,
                                             label = !!paste("Autogenerated by", attr(step, "label"))) ))
    attr(new_step, "auto-generated") <- TRUE
    
    # warning("Attempting to inject a `declare_reveal(", this_step_meta$outcome_variables, ", ",
    #         this_step_meta$assignment_variables,
    #         ")` step after PO (", attr(step, "label"),
    #         ")", call. = FALSE)
    
    design <- insert_step(design, new_step, after = i)
    return(design)
  }
  
  unassigned_vars <- check("assignment_variables", "assignment", "assignment_variables", from = i + 1)
  unrevealed_vars <- check("assignment_variables", "reveal", "outcome_variables", from = i + 1)
  
  cant_find <- prev_unassigned %i% prev_unrevealed %i% unassigned_vars %i% unrevealed_vars
  
  
  new_step <- eval_tidy(quo(declare_reveal(outcome_variables = !!this_step_meta$outcome_variables,
                                           assignment_variables = !!this_step_meta$assignment_variables,
                                           label = !!paste("Autogenerated by", attr(step, "label"))) ))
  attr(new_step, "auto-generated") <- TRUE
  
  for (step_j in design[length(design):(i + 1)]) {
    if (is.null(step_meta <- attr(step_j, "step_meta"))) next;
    if (attr(step_j, "step_type") == "assignment") {
      if (any(step_meta$assignment_variables %in% attr(step, "step_meta")$assignment_variables)) {
        design <- insert_step(design, new_step, after = step_j)
        break;
      }
    }
    else if (attr(step_j, "step_type") == "reveal") {
      if (any(step_meta$outcome_variables %in% attr(step, "step_meta")$assignment_variables)) {
        design <- insert_step(design, new_step, after = step_j)
        break;
      }
    }
  }
  
  design
  
}



#' @importFrom fabricatr fabricate add_level modify_level
#' @rdname declare_potential_outcomes
potential_outcomes.NULL <- function(formula=stop("Not provided"), ..., data, level = NULL) {
  
  if (is.character(level)) {
    fabricate(data = data,!!level := modify_level(...))
  } else {
    fabricate(data = data, ..., ID_label = NA)
  }
}

validation_fn(potential_outcomes.NULL) <- function(ret, dots, label){
  if ("ID_label" %in% names(dots)) {
    declare_time_error("Must not pass ID_label.", ret)
  }
  
  if ("" %in% names(dots)) {
    declare_time_warn("Unnamed declared argument in potential outcome", ret)
  }
  
  ret
}




#' Expand assignment conditions
#'
#' Internal helper to eagerly build assignment conditions for potential outcomes.
#'
#' If conditions is a data.frame, it is returned unchanged
#'
#' Otherwise, if conditions is a list, it is passed to expand.grid for expansion to a data.frame
#'
#' Otherwise, if condition is something else, box it in a list with assignment_variables for names,
#' and pass that to expand.grid.
#'
#' @param conditions the conditions
#' @param assignment_variables the name of assignment variables, if conditions is not already named.
#' @return a data.frame of potential outcome conditions
#' @keywords internal
expand_conditions <- function() {
  if (!is.data.frame(conditions)) {
    if (!is.list(conditions)) {
      conditions <- rep(list(conditions), length(assignment_variables))
      conditions <- setNames(conditions, assignment_variables)
    }
    
    conditions <- expand.grid(conditions, stringsAsFactors = FALSE)
  }
  conditions
}
formals(expand_conditions) <- formals(potential_outcomes.formula)
formals(expand_conditions)["label"] <- list(NULL) # Fixes R CMD Check warning outcome is undefined