Sensitivity of DiD estimate to parallel-trends violations — morie_did_sensitivity

For each \(\delta\), computes a bias-adjusted confidence set under the bound \(|\mathrm{bias}| \le \delta \hat\sigma\) (Rambachan & Roth, 2023, conservative version).

Usage

morie_did_sensitivity_analysis(
  data,
  outcome,
  treatment,
  post,
  covariates = NULL,
  delta_range = NULL,
  cluster = NULL,
  alpha = 0.05
)

Arguments

data: A data frame containing the outcome, treatment, post and any covariate columns.
outcome: Name of the outcome column.
treatment: Name of the binary (0/1) treatment-group column.
post: Name of the binary (0/1) post-period column.
covariates: Optional character vector of covariate column names.
delta_range: Numeric vector of \(\delta\) values to evaluate (default seq(0, 2, 0.25)).
cluster: Optional cluster ID column for CR1 standard errors.
alpha: Significance level for confidence intervals (default 0.05).

Value

A data frame with columns delta, ci_lower, ci_upper, covers_zero.

Details

For the full Rambachan-Roth fixed-length-confidence-interval (FLCI) procedure with event-time pre-trends prefer HonestDiD::createSensitivityResults_relativeMagnitudes on an event-study coefficient vector.

References

Rambachan, A., & Roth, J. (2023). A more credible approach to parallel trends. Review of Economic Studies, 90(5), 2555–2591.