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In-Time Placebo (Backdating) Test for SCM

Source: R/scm.R
placebo_in_time.Rd

Re-estimates the synthetic control after artificially backdating the treatment to a pre-treatment period, following Abadie, Diamond & Hainmueller (2015) and Abadie & Vives-i-Bastida (2022, principle 7: "out-of-sample validation is key"). Only pre-treatment data enter the exercise, so the placebo gap after t0_placebo is uncontaminated by the actual intervention. A credible design shows no sizable divergence at the backdated treatment time.

Usage

placebo_in_time(fit, t0_placebo = NULL)

Arguments

fit

A sharp coresynth object from scm_fit() with method = "scm".

t0_placebo

Backdated treatment period as a 1-based position in fit$times; must satisfy 2 <= t0_placebo < T_pre. Default floor(T_pre / 2).

Value

A list with:

  • t0_placebo: the backdated treatment period used

  • times: time values of the pre-treatment window

  • unit_weights: placebo donor weights

  • Y_treat, Y_synth, gap: series over the pre-treatment window

  • placebo_att: mean placebo gap over (t0_placebo, T_pre]

  • fit_rmspe: RMSPE over the placebo fitting window 1..t0_placebo

  • eval_rmspe: RMSPE over the placebo post window (t0_placebo, T_pre]

Details

The refit uses the outcomes of periods 1..t0_placebo as predictors (the predictors = NULL default), regardless of how the original fit was specified, because user-supplied pred() windows cannot be lagged automatically (ADH 2015 lag their predictors by hand).

See also

mspe_ratio_pval() for in-space placebos, loo_donors() for donor-robustness checks.