Estimate ATT(g,t) with Bad Controls — didbc • badcontrols

Main function for difference-in-differences with bad controls. Wraps the ptetools infrastructure to handle time-varying covariates affected by treatment. Supports both imputation and doubly robust ML estimation methods.

Usage

didbc(
  yname,
  gname,
  tname,
  idname = NULL,
  data,
  bad_control_formula = NULL,
  xformula = ~1,
  d_covs_formula = NULL,
  bad_control_cov_formula = NULL,
  bad_control_d_cov_formula = NULL,
  est_method = c("imputation", "dr_ml"),
  bad_control_identification_strategy = c("unconfoundedness", "did"),
  nuisance_method = c("ml", "parametric"),
  control_group = "notyettreated",
  anticipation = 0,
  base_period = "varying",
  weightsname = NULL,
  cband = TRUE,
  alp = 0.05,
  boot_type = "multiplier",
  bstrap = TRUE,
  cl = 1,
  biters = 100,
  nfolds = 5,
  overlap_threshold = 0.99,
  min_group_size = 5,
  num_threads = NULL,
  ...
)

Arguments

yname: Name of the outcome variable (string)
gname: Name of the group variable (string). Should be 0 for never-treated units and the period of first treatment for treated units.
tname: Name of the time period variable (string)
idname: Name of the unit identifier variable (string); default NULL, matching ptetools::pte
data: A balanced panel data.frame
bad_control_formula: One-sided formula naming exactly one bad control variable (a time-varying covariate affected by treatment), e.g. ~X. NULL (the default) means no bad control at all, in which case didbc reduces to plain DiD with covariates. Multiple bad controls are not yet supported.
xformula: One-sided formula for general exogenous covariates, entered as their pre-treatment-period level (default ~1)
d_covs_formula: One-sided formula for general exogenous covariates, entered as their change (post minus pre) rather than a level. Default NULL (unused).
bad_control_cov_formula: One-sided formula for auxiliary covariates (W in the paper) used to model the bad control's counterfactual evolution, entered as their pre-treatment-period level. Any variable name can be used here, including the outcome itself (e.g. ~Y reproduces the old "lagged outcome as W" behavior). Default NULL (unused, and ignored if bad_control_formula is NULL).
bad_control_d_cov_formula: Like bad_control_cov_formula, but entered as a change (post minus pre) rather than a level. Default NULL (unused).
est_method: Estimation method: "imputation" (default) for the two-step imputation approach, or "dr_ml" for the doubly robust estimator.
bad_control_identification_strategy: Which assumption identifies the bad control's untreated potential evolution: "unconfoundedness" (default), i.e. Covariate Unconfoundedness for the bad control given its own pre-period level and (W, Z), or "did", i.e. parallel trends for the bad control itself given (W, Z) (app:bad-control-parallel-trends in the supplementary appendix). "did" is only supported for est_method = "imputation", requires bad_control_formula to be non-NULL, and does not distinguish a binary bad control.
nuisance_method: For est_method = "dr_ml" only: "ml" (default) estimates the doubly robust estimator's nuisance functions via cross-fitted random forests; "parametric" estimates them via OLS/logistic regression, assuming correct specification. Ignored for est_method = "imputation".
control_group: Which units serve as controls: "notyettreated" (default) or "nevertreated". Passed to ptetools::two_by_two_subset.
anticipation: Number of periods before treatment where it can already affect the outcome (default 0). Passed to ptetools::two_by_two_subset.
base_period: Which pre-period to compare each post-period to: "varying" (default) or "universal". Passed to ptetools::two_by_two_subset.
weightsname: Name of a sampling-weights variable in data (default NULL)
cband: Logical; compute a uniform confidence band instead of pointwise confidence intervals (default TRUE)
alp: Significance level (default 0.05)
boot_type: "multiplier" (default) or "empirical"
bstrap: Logical; whether to use the multiplier bootstrap (TRUE, the default) or purely analytical standard errors (FALSE) when the estimator returns an influence function. bstrap = FALSE only supports pointwise confidence intervals.
cl: Number of clusters for parallel computing in the bootstrap (default 1)
biters: Number of bootstrap iterations (default 100)
nfolds: Number of cross-fitting folds for DR/ML (default 5)
overlap_threshold: For est_method = "dr_ml" only: if any unit's fitted propensity (from a preliminary, non-cross-fit fit) exceeds this value in a (g,t) cell, estimation falls back to the imputation estimator for that cell, with a warning naming the group, time period, and offending unit IDs. Default 0.99.
min_group_size: For est_method = "dr_ml" only: if a (g,t) cell has fewer treated units than the number of propensity-score covariates plus min_group_size, estimation falls back to the imputation estimator for that cell, with a warning naming the group, time period, and treated count – a separate trigger from overlap_threshold, since fitting the propensity score needs a nontrivial treated sample to identify at all, relative to how many covariates it has to fit, which can fail even under good covariate overlap. Default 5, matching did's own convention for an analogous check.
num_threads: For est_method = "dr_ml", nuisance_method = "ml" only: number of threads for grf's forests. Default NULL uses grf's own auto-detection (all available cores). Set to 1 to pin a single thread per forest – e.g. for Monte Carlo work, where many reps are run in outer parallelism (one core each) rather than letting each individual forest fit claim every core on the machine.
...: Additional arguments passed to ptetools::pte

Value

A pte_results object containing:

overall_att: Overall ATT estimate and SE
att_gt: Group-time specific ATT estimates

Details

This function implements the methods from Caetano, Callaway, Payne, and Sant'Anna (2024). Two estimation approaches are available:

Imputation (est_method = "imputation"):

Among controls, learn X_t ~ f(X(t-1), W, Z)
For treated, predict counterfactual X_t(0)
Run DiD using imputed X_t(0) instead of observed X_t

Doubly Robust ML (est_method = "dr_ml"):

Estimate four nuisance functions via random forests
Combine into a doubly robust score with cross-fitting
Returns influence function for fast inference

Requires the grf package. Consistent if either the outcome regression or the propensity score is correctly specified.

References

Caetano, C., Callaway, B., Payne, S., and Sant'Anna, H. (2024). "Difference-in-Differences with Bad Controls."

Examples

# Simulate data
sim <- simulate_bad_controls(n = 500, T_max = 4)
head(sim$data)
#>   id period G D          Y          X          Z          W
#> 1  1      1 0 0 -1.4802918 -0.9656533 -1.4000435 -1.0565754
#> 2  1      2 0 0 -1.0115477 -1.0414870 -1.4000435 -1.0565754
#> 3  1      3 0 0 -0.3547090 -0.8741843 -1.4000435 -1.0565754
#> 4  1      4 0 0 -2.7886557 -1.9645233 -1.4000435 -1.0565754
#> 5  2      1 3 0  2.1272719  0.4692818  0.2553171  0.5631701
#> 6  2      2 3 0  0.9515011  0.4160736  0.2553171  0.5631701

# Imputation approach
# \donttest{
res_imp <- didbc(
  yname = "Y", gname = "G", tname = "period", idname = "id",
  data = sim$data,
  bad_control_formula = ~X,
  xformula = ~Z,
  est_method = "imputation"
)
#> Warning: critical value for uniform confidence band is somehow smaller than
#>             critical value for pointwise confidence interval...using pointwise
#>             confidence interal
#> Warning: critical value for uniform confidence band is somehow smaller than
#>             critical value for pointwise confidence interval...using pointwise
#>             confidence interal
#> Warning: critical value for uniform confidence band is somehow smaller than
#>             critical value for pointwise confidence interval...using pointwise
#>             confidence interal
#> Warning: critical value for uniform confidence band is somehow smaller than
#>             critical value for pointwise confidence interval...using pointwise
#>             confidence interal
# }