## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

## ----simulate-data------------------------------------------------------------
library(causaldef)
set.seed(42)

n <- 500

# Unmeasured confounder
U <- rnorm(n)

# Observed covariate (partially captures U)
W <- 0.7 * U + rnorm(n, sd = 0.5)

# Treatment assignment (confounded by U via W)
ps_true <- plogis(0.3 + 0.8 * U)
A <- rbinom(n, 1, ps_true)

# True causal effect
beta_true <- 2.0

# Outcome (affected by A and U)
Y <- 1 + beta_true * A + 1.5 * U + rnorm(n)

# Negative control outcome (affected by U only, NOT by A)
Y_nc <- 0.5 + 1.2 * U + rnorm(n, sd = 0.8)

# Create data frame
df <- data.frame(W = W, A = A, Y = Y, Y_nc = Y_nc)

## ----create-spec--------------------------------------------------------------
spec <- causal_spec(
  data = df,
  treatment = "A",
  outcome = "Y",
  covariates = "W",
  negative_control = "Y_nc"
)

print(spec)

## ----nc-diagnostic, eval=FALSE------------------------------------------------
# nc_result <- nc_diagnostic(
#   spec,
#   method = "iptw",
#   alpha = 0.05,
#   n_boot = 200
# )
# 
# print(nc_result)

## ----scenario-success, eval=FALSE---------------------------------------------
# # When W = U (no unmeasured confounding)
# df_full <- df
# df_full$W <- U  # Perfect proxy
# 
# spec_full <- causal_spec(
#   df_full, "A", "Y", "W", negative_control = "Y_nc"
# )
# 
# nc_full <- nc_diagnostic(spec_full, method = "iptw", n_boot = 100)
# print(nc_full)
# # Expect: falsified = FALSE

## ----scenario-fail, eval=FALSE------------------------------------------------
# # When W is noise (no information about U)
# df_bad <- df
# df_bad$W <- rnorm(n)  # Useless proxy
# 
# spec_bad <- causal_spec(
#   df_bad, "A", "Y", "W", negative_control = "Y_nc"
# )
# 
# nc_bad <- nc_diagnostic(spec_bad, method = "iptw", n_boot = 100)
# print(nc_bad)
# # Expect: falsified = TRUE

## ----combined-workflow, eval=FALSE--------------------------------------------
# # Step 1: Estimate deficiency
# def_results <- estimate_deficiency(
#   spec,
#   methods = c("unadjusted", "iptw", "aipw"),
#   n_boot = 100
# )
# 
# print(def_results)
# 
# # Step 2: Run negative control diagnostic on best method
# best_method <- names(which.min(def_results$estimates))
# nc_check <- nc_diagnostic(spec, method = best_method, n_boot = 100)
# 
# # Step 3: Compute policy bounds if assumptions not falsified
# if (!nc_check$falsified) {
#   bounds <- policy_regret_bound(
#     def_results,
#     utility_range = c(-5, 10),
#     method = best_method
#   )
#   print(bounds)
# } else {
#   warning("Causal assumptions falsified. Consider additional covariates.")
# }

## ----kappa-estimation, eval=FALSE---------------------------------------------
# # If you believe Y' has 80% of Y's confounding structure:
# nc_tight <- nc_diagnostic(
#   spec,
#   method = "iptw",
#   kappa = 0.8,
#   n_boot = 100
# )
# 
# print(nc_tight)