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Compare drlate estimators in one call

Source: R/compare.R
drlate_compare.Rd

Runs several estimators on the same specification and collects the causal estimates with their confidence intervals — the sensitivity comparison applied papers routinely report. Formula restrictions are handled automatically: method = "ipw" drops the outcome/treatment covariates and method = "ra" drops the instrument covariates (each with a message), matching the requirements of those estimators.

Usage

drlate_compare(
  outcome,
  treatment,
  instrument,
  data,
  methods = c("ipwra", "ipw", "aipw", "ra"),
  both_norms = FALSE,
  ...
)

Arguments

outcome

A formula y ~ covariates for the outcome model. Use y ~ 1 for no covariates (required when method = "ipw").

treatment

A formula d ~ covariates for the treatment model.

instrument

A formula z ~ covariates for the instrument propensity score model; z must be binary 0/1. Use z ~ 1 when method = "ra".

data

A data frame containing all variables.

methods

Estimators to run (any of the method values accepted by drlate()).

both_norms

Logical; also run the unnormalized variants of "ipw" and "aipw" (default FALSE).

...

Passed on to drlate() (e.g. omodel, tmodel, ivmodel, estimand, weights, cluster).

Value

An object of class "drlate_compare": a data frame with columns method, normalized, estimate, se, ci_lo, ci_hi, with a print method and a dot-whisker plot method.

Details

Because IPW carries no outcome/treatment regressions and RA carries no instrument propensity score, the automatic formula adjustment means the rows do not share a single adjustment specification: differences between the IPW or RA row and the doubly robust rows reflect both the estimator and the reduced specification. Read the comparison as a robustness display, not as a test that isolates estimator choice; the doubly robust rows (IPWRA, AIPW) are the like-for-like pair.

Examples

cmp <- drlate_compare(lwage ~ age + educ, nvstat ~ age + educ,
                      rsncode ~ age + educ, data = drlate_sim)
#> method = "ipw": dropping outcome/treatment covariates (weighted means only).
#> method = "ra": dropping instrument covariates (no propensity score).
cmp
#> Estimator comparison (LATE)
#> 
#>   estimator estimate     se           95% CI
#>       ipwra   0.4705 0.0792 [0.3153, 0.6256]
#>   ipw (nrm)   0.4741 0.0793 [0.3187, 0.6295]
#>  aipw (nrm)   0.4702 0.0792 [0.3150, 0.6254]
#>          ra   0.4597 0.0792 [0.3045, 0.6150]