Overview | What this vignette covers | Step 1: Define the DGP | Step 2: Inspect a Draw | Step 3: Define Estimators | Step 4: Evaluate Each Estimator | Step 5: Vary Sample Size with causalsim_eval_grid() | Step 6: Vary Confounding Strength | Step 7: Heterogeneous Effects (Effect Modifiers) | Where to go next | Session info
Package: causalsim 0.1.0
Chayce Reed
causalsim: Simulation-Ready Causal Data Generating Processes
Construct, simulate, and evaluate causal data generating processes (DGPs) with known ground truth. Designed for benchmarking causal estimators, studying confounding and treatment-effect heterogeneity, and building reproducible teaching examples. Covariate roles (confounder, effect modifier, noise) and heterogeneous treatment effects are first-class concepts in the API, and estimator performance is summarised with bias, root mean squared error, confidence-interval coverage, and power.
Authors:
causalsim_0.1.0.tar.gz
causalsim_0.1.0.zip(r-4.7-any)causalsim_0.1.0.zip(r-4.6-any)causalsim_0.1.0.zip(r-4.5-any)
causalsim_0.1.0.tgz(r-4.6-any)causalsim_0.1.0.tgz(r-4.5-any)
causalsim_0.1.0.tar.gz(r-4.7-any)causalsim_0.1.0.tar.gz(r-4.6-any)
causalsim_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
causalsim/json (API)
| # Install 'causalsim' in R: |
| install.packages('causalsim', repos = c('https://chaycereed.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/chaycereed/causalsim/issues
Pkgdown/docs site:https://chaycereed.github.io
Last updated from:9898ab85c5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 157 | ||
| source / vignettes | OK | 248 | ||
| linux-release | OK | 105 | ||
| macos-release | OK | 89 | ||
| macos-oldrel | OK | 89 | ||
| windows-devel | OK | 72 | ||
| windows-release | OK | 62 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 101 |
Exports:causalsimcausalsim_covarcausalsim_dgpcausalsim_drawcausalsim_evalcausalsim_eval_grid
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simulate a causal dataset with known ground truth | causalsim |
| Define a covariate for a causal DGP | causalsim_covar |
| Create a causal data generating process | causalsim_dgp |
| Draw a dataset from a causal DGP | causalsim_draw |
| Evaluate a causal estimator against a known DGP | causalsim_eval |
| Evaluate an estimator across a parameter grid | causalsim_eval_grid |
| Plot the distribution of estimates from a causalsim_eval result | plot.causalsim_eval |
| Summarise a causalsim_eval result | summary.causalsim_eval |