Efficient Online Estimation of Causal Effects by Deciding What to Observe
Authors: Shantanu Gupta, Zachary Lipton, David Childers
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate our findings experimentally 1 (Section 6). We validate our methods on synthetic data generated from known causal graphs (see Appendix D for parameter values and moment conditions used). |
| Researcher Affiliation | Academia | Shantanu Gupta, Zachary C. Lipton, David Childers Carnegie Mellon University {shantang,zlipton,dchilders}@cmu.edu |
| Pseudocode | Yes | Figure 1: Algorithms for OMS-ETC and OMS-ETG. |
| Open Source Code | Yes | The code and data are available at https://www.github.com/acmi-lab/ online-moment-selection. |
| Open Datasets | Yes | Finally, we demonstrate the effectiveness of our methods on two semi-synthetic datasets: the Infant Health Development Program (IHDP) dataset [19] and a Vietnam era draft lottery dataset [2]. Replication data for: Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records, 2009. URL https://doi.org/10. 7910/DVN/PLF0YL. |
| Dataset Splits | No | No specific train/validation/test dataset splits (e.g., percentages, sample counts, or explicit standard splits) are mentioned. The paper describes generating semi-synthetic data and states 'The MSE values are computed across 12, 000 runs.' |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names like 'PyTorch 1.9' or solver versions like 'CPLEX 12.4') are provided. The paper only mentions using a 'regularized GMM weight matrix' and implies Python use given the GitHub link. |
| Experiment Setup | No | The paper mentions 'For OMS-ETG, we use the regularized GMM weight matrix with λW := 0.01' and 'The MSE values are computed across 12, 000 runs'. However, it does not provide specific hyperparameter values for model training (e.g., learning rates, batch sizes, epochs) or detailed system-level configurations. |