Draw a dataset from a causal DGP — causalsim

Simulates one dataset from a causalsim_dgp() object using the structural model:

Usage

causalsim_draw(dgp, seed = NULL)

Arguments

dgp: A causalsim_dgp object created by causalsim_dgp().
seed: Integer or NULL. If non-null, passed to set.seed() before generating any random values, making the draw reproducible. Default NULL.

Value

A data frame with dgp$n rows and the following columns:

Covariate columns: One column per covariate in dgp$covar_spec, named to match the spec (e.g. W, W1, Z).
A: Binary treatment indicator (0/1).
Y: Observed outcome.
.tau: Individual treatment effect (CATE) — ground truth.
.p: Individual propensity score — ground truth.

The .tau and .p columns carry individual-level ground truth and are prefixed with . to distinguish them from observed variables. causalsim_eval() uses them directly without re-estimation.

Details

W ~ covariate_spec
A ~ Bernoulli(propensity(W))
Y  = baseline(W) + effect(W) * A + N(0, sigma^2)

Examples

dgp <- causalsim_dgp(n = 500, n_confounders = 1, effect = 2)
d   <- causalsim_draw(dgp, seed = 1)
head(d)
#>            W A          Y .tau        .p
#> 1 -0.6264538 0 -1.4546914    2 0.4223273
#> 2  0.1836433 0 -0.8456543    2 0.5229393
#> 3 -0.8356286 0 -1.2504797    2 0.3970399
#> 4  1.5952808 0  0.6672881    2 0.6894695
#> 5  0.3295078 1  0.7092303    2 0.5410956
#> 6 -0.8204684 0 -2.0350035    2 0.3988560

# Reproducible draws
d1 <- causalsim_draw(dgp, seed = 42)
d2 <- causalsim_draw(dgp, seed = 42)
identical(d1, d2)  # TRUE
#> [1] TRUE