Matching a Desired Causal State via Shift Interventions
Authors: Jiaqi Zhang, Chandler Squires, Caroline Uhler
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In line with our theoretical results, we also demonstrate experimentally that our proposed active learning strategies require fewer interventions compared to several baselines. [...] We now evaluate our algorithms in several synthetic settings. |
| Researcher Affiliation | Academia | Jiaqi Zhang LIDS, EECS, and IDSS, MIT viczhang@mit.edu Chandler Squires LIDS, EECS, and IDSS, MIT csquires@mit.edu Caroline Uhler LIDS, EECS, and IDSS, MIT cuhler@mit.edu |
| Pseudocode | Yes | Algorithm 1: Active Learning for Causal Mean Matching |
| Open Source Code | Yes | Code is publicly available at: https://github.com/uhlerlab/causal_mean_matching. |
| Open Datasets | No | The paper describes generating '100 problem instances' in 'synthetic settings' using graph generation models (Erdös-Rényi, Barabási Albert, etc.) but does not specify a publicly available or open dataset that was used for training. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It describes generating problem instances for evaluation. |
| Hardware Specification | No | The paper states that experiments were run in 'synthetic settings' but does not provide any specific hardware details such as GPU/CPU models or memory used. |
| Software Dependencies | No | While the paper provides a link to its code repository, it does not explicitly list any software dependencies with specific version numbers within the paper's text. |
| Experiment Setup | Yes | Each setting considers a particular graph type, number of nodes p in the graph and number of perturbation targets |I | p in the matching intervention. We generate 100 problem instances in each setting. [...] The graph size p in our simulations ranges from 10 to 1000, while the number of perturbation targets ranges from 1 to min{p, 100}. [...] Each algorithm is run with sparsity constraint S = 1. [...] Finally, we consider the effect of the sparsity constraint S in Figure 5c with |I | = 50. |