| | --- |
| | license: bigscience-openrail-m |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # rerandomization-benchmarks |
| |
|
| | Replication dataset for the benchmark and diagnostic analyses in |
| | **Goldstein, Jerzak, Kamat & Zhu (2025), _“fastrerandomize: Fast Rerandomization Using Accelerated Computing”_.** |
| |
|
| | --- |
| |
|
| | ## Project & Paper Links |
| |
|
| | - **Paper (preprint):** <https://arxiv.org/abs/2501.07642> |
| | - **Software repository:** <https://github.com/cjerzak/fastrerandomize-software> |
| | - **Package name:** `fastrerandomize` (R) |
| |
|
| | --- |
| |
|
| | ## What’s in this dataset? |
| |
|
| | The dataset contains **simulation-based benchmark results** used to compare: |
| |
|
| | - Different **hardware backends** |
| | - `M4-CPU` (Apple M4 CPU, via JAX/XLA) |
| | - `M4-GPU` (Apple M4 GPU / METAL) |
| | - `RTX4090` (NVIDIA CUDA GPU) |
| | - `BaseR` (non-accelerated R baseline) |
| | - `jumble` (the `jumble` package as an alternative rerandomization implementation) |
| |
|
| | - Different **problem scales** |
| | - Sample sizes: `n_units ∈ {10, 100, 1000}` |
| | - Covariate dimensions: `k_covars ∈ {10, 100, 1000}` |
| | - Monte Carlo draw budgets: `maxDraws ∈ {1e5, 2e5}` |
| | - Exact vs approximate linear algebra: `approximate_inv ∈ {TRUE, FALSE}` |
| |
|
| | - Different **rerandomization specifications** |
| | - Acceptance probability targets (via `randomization_accept_prob`) |
| | - Use or non-use of fiducial intervals (`findFI`) |
| |
|
| | Each row corresponds to a particular Monte Carlo configuration and summarizes: |
| |
|
| | 1. **Design & simulation settings** (e.g., `n_units`, `k_covars`, `maxDraws`, `treatment_effect`) |
| | 2. **Performance metrics** (e.g., runtime for randomization generation and testing) |
| | 3. **Statistical diagnostics** (e.g., p-value behavior, coverage, FI width) |
| | 4. **Hardware & system metadata** (CPU model, number of cores, OS, etc.) |
| |
|
| | These data were used to: |
| |
|
| | - Produce the **runtime benchmark figures** (CPU vs GPU vs baseline R / `jumble`) |
| | - Compute **speedup factors** and **time-reduction summaries** |
| | - Feed into macros such as `\FRRMaxSpeedupGPUvsBaselineOverall`, `\FRRGPUVsCPUTimeReductionDthousandPct`, etc., which are then read from `./Figures/bench_macros.tex` in the paper. |
| |
|
| | --- |
| |
|
| | ## Files & Structure |
| |
|
| | *(Adjust this section to match exactly what you upload to Hugging Face; here is a suggested structure.)* |
| |
|
| | - `VaryNAndD_main.csv` |
| | Aggregated benchmark/simulation results across all configurations used in the paper. |
| |
|
| | - `VaryNAndD_main.parquet` (optional) |
| | Parquet version of the same table (faster to load in many environments). |
| |
|
| | - `CODE/` (optional, if you choose to include) |
| | - `FastSRR_VaryNAndD.R` |
| | - `FastRR_PlotFigs.R` |
| | Exact R scripts used to generate the raw CSV files and figures. |
| |
|
| | --- |
| |
|
| | ## Main Columns (schema overview) |
| |
|
| | Below is an overview of the most important columns you will encounter in `VaryNAndD_main.*`. |
| | Names are taken directly from the R code (especially the `res <- as.data.frame(cbind(...))` section in `FastSRR_VaryNAndD.R` and the subsequent processing in `FastRR_PlotFigs.R`). |
| |
|
| | ### Core design variables |
| |
|
| | - `treatment_effect` – Constant treatment effect used in the simulation (e.g., `0.1`). |
| | - `SD_inherent` – Baseline SD of potential outcomes (`SD_inherent` in `GenerateCausalData`). |
| | - `n_units` – Total number of experimental units. |
| | - `k_covars` – Number of covariates. |
| | - `maxDraws` – Maximum number of candidate randomizations drawn (e.g., `1e5`, `2e5`). |
| | - `findFI` – Logical (`TRUE`/`FALSE`): whether fiducial intervals were computed. |
| | - `approximate_inv` – Logical (`TRUE`/`FALSE`): whether approximate inverse / stabilized linear algebra was used. |
| | - `Hardware` – Hardware / implementation label, recoded in `FastRR_PlotFigs.R` to: |
| | - `"M4-CPU"` (was `"CPU"`) |
| | - `"M4-GPU"` (was `"METAL"`) |
| | - `"RTX4090"` (was `"NVIDIA"`) |
| | - `"jumble"` (was `"AltPackage"`) |
| | - `"BaseR"` (pure R baseline) |
| | - `monte_i` – Monte Carlo replication index. |
| |
|
| | ### Rerandomization configuration |
| |
|
| | - `prob_accept` – Target acceptance probability (`randomization_accept_prob`). |
| | - `accept_prob` – Same or related acceptance probability field (used within plotting code). |
| |
|
| | ### Randomization-test & FI summaries |
| |
|
| | These are typically aggregated across Monte Carlo replications and/or over covariate-dimension strata: |
| |
|
| | - `p_value` – Mean p-value across replications, by `k_covars` and acceptance probability. |
| | - `p_value_se` – Standard error of the above p-value estimates. |
| | - `min_p_value` – Average minimum achievable p-value (`1/(1 + n_accepted)`), reflecting how many accepted randomizations were available. |
| | - `number_successes` – Average number of accepted randomizations (per configuration). |
| | - `tau_hat_mean` – Mean estimated treatment effect across replications. |
| | - `tau_hat_var` – Variance of the estimated treatment effect across replications. |
| | - `FI_lower_vec`, `FI_upper_vec` – Mean lower/upper endpoints of fiducial intervals. |
| | - `FI_width` – Median width of the fiducial interval (where available). |
| | - `truth_covered` – Average indicator for whether the interval covered the true treatment effect. |
| |
|
| | ### Estimator-selection diagnostics (acceptance-prob “minimization”) |
| |
|
| | These summarize how well different strategies for choosing the optimal acceptance probability perform: |
| |
|
| | - `colMeans_mean_p_value_matrix`, `colMeans_median_p_value_matrix`, `colMeans_modal_p_value_matrix` – |
| | Average p-value summaries used to define estimators of the “best” acceptance probability. |
| |
|
| | - `bias_select_p_via_mean`, `rmse_select_p_via_mean` – |
| | Bias and RMSE when selecting the acceptance probability based on the mean p-value. |
| |
|
| | - `bias_select_p_via_median`, `rmse_select_p_via_median` – |
| | Bias and RMSE when selecting the acceptance probability based on the median p-value. |
| |
|
| | - `bias_select_p_via_mode`, `rmse_select_p_via_mode` – |
| | Bias and RMSE when selecting the acceptance probability based on the modal p-value. |
| |
|
| | - `bias_select_p_via_baseline`, `rmse_select_p_via_baseline` – |
| | Bias and RMSE of a naive baseline strategy (e.g., choosing acceptance probability at random), used as a comparison. |
| |
|
| | ### Timing and hardware metadata |
| |
|
| | Timing quantities are used to produce the benchmark plots in the paper: |
| |
|
| | - `t_GenerateRandomizations` – Time (seconds) spent generating randomization pools. |
| | - `t_RandomizationTest` – Time (seconds) spent on randomization-based inference. |
| | - `randtest_time` – Duplicated / convenience version of `t_RandomizationTest` in some contexts. |
| | - `sysname`, `machine`, `hardware_version` – OS and machine-level metadata (`Sys.info()`). |
| | - `nCores` – Number of CPU cores from `benchmarkme::get_cpu()`. |
| | - `cpuModel` – CPU model name from `benchmarkme::get_cpu()`. |
| |
|
| | > **Note:** Because the scripts were developed iteratively, some columns may appear duplicated or with slightly redundant naming (e.g., multiple `randtest_time`-like fields). For replication of the paper’s figures, these are harmless; users may drop redundant columns as needed. |
| | |
| | --- |
| | |
| | ## How to use the dataset |
| | |
| | ### In Python (via `datasets`) |
| | |
| | ```python |
| | from datasets import load_dataset |
| |
|
| | ds = load_dataset("YOUR_USERNAME/rerandomization-benchmarks", split="train") |
| | print(ds) |
| | print(ds.column_names) |
| | ```` |
| | |
| | Or directly with `pandas`: |
| | |
| | ```python |
| | import pandas as pd |
| | |
| | df = pd.read_csv("VaryNAndD_main.csv") |
| | df.head() |
| | ``` |
| | |
| | ### In R |
| | |
| | ```r |
| | library(data.table) |
| | |
| | bench <- fread("VaryNAndD_main.csv") |
| | str(bench) |
| |
|
| | # Example: reproduce summaries by hardware and problem size |
| | bench[, .( |
| | mean_t_generate = mean(t_GenerateRandomizations, na.rm = TRUE), |
| | mean_t_test = mean(t_RandomizationTest, na.rm = TRUE) |
| | ), by = .(Hardware, n_units, k_covars, maxDraws, approximate_inv)] |
| | ``` |
| | |
| | You can then: |
| | |
| | * Recreate runtime comparisons across hardware platforms. |
| | * Explore how acceptance probability, dimension, and sample size interact. |
| | * Use the timing information as inputs for your own design/planning calculations. |
| | |
| | --- |
| | |
| | ## Citation |
| | |
| | If you use this dataset, **please cite the main paper**: |
| | |
| | ```bibtex |
| | @misc{goldstein2025fastrerandomizefastrerandomizationusing, |
| | title = {fastrerandomize: Fast Rerandomization Using Accelerated Computing}, |
| | author = {Rebecca Goldstein and Connor T. Jerzak and Aniket Kamat and Fucheng Warren Zhu}, |
| | year = {2025}, |
| | eprint = {2501.07642}, |
| | archivePrefix= {arXiv}, |
| | primaryClass = {stat.CO}, |
| | url = {https://arxiv.org/abs/2501.07642} |
| | } |
| | ``` |
| | |
| | --- |
| | |
| | ## Contact |
| | |
| | For questions about the paper, software, or dataset: |
| | |
| | * Corresponding author: **Connor T. Jerzak** – [connor.jerzak@austin.utexas.edu](mailto:connor.jerzak@austin.utexas.edu) |
| | * Issues & contributions: please use the GitHub repository issues page for `fastrerandomize`. |
| | |
| | --- |
