NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Authors: Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets. We perform comprehensive empirical studies on multiple benchmark datasets where NESTER outperforms existing state-of-the-art models. The results shown in Tab 2 show the superior performance of NESTER over existing methods. |
| Researcher Affiliation | Academia | Abbavaram Gowtham Reddy, Vineeth N Balasubramanian Indian Institute of Technology Hyderabad, India cs19resch11002@iith.ac.in, vineethnb@iith.ac.in |
| Pseudocode | Yes | We outline our overall algorithm in Algorithm 1 of Appendix B. |
| Open Source Code | Yes | Our code and instructions to reproduce the results are included in the supplementary material and will be made publicly available. |
| Open Datasets | Yes | Thus, following (Shalit, Johansson, and Sontag 2017; Yoon, Jordon, and van der Schaar 2018; Shi, Blei, and Veitch 2019; Farajtabar et al. 2020), we experiment on two semi-synthetic datasets Twins (Almond, Chay, and Lee 2005), IHDP (Hill 2011) that are derived from real-world RCTs (see Appendix C for details). We also experiment on one real-world dataset Jobs (La Londe 1986). |
| Dataset Splits | Yes | For both datasets (IHDP, Twins), following prior works (Shalit, Johansson, and Sontag 2017; Shi, Blei, and Veitch 2019; Yoon, Jordon, and van der Schaar 2018), we use the standard train-test split for 100 random instances of the data. The number of samples for IHDP is 747, and for Twins is 118400. In both cases, the train-test split is 80-20. For Jobs dataset, following (Shalit, Johansson, and Sontag 2017), we consider 10 random train-test splits on the total number of 445 samples with a split ratio of 80-20. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, TensorFlow, etc.) needed to replicate the experiment. |
| Experiment Setup | Yes | To permit efficient learning (and to some degree, interpretability of the learned program, as discussed in Appendix G), we limit the program depth to utmost 5 for the main experiments. For both NESTER-NEAR and NESTER-d Pads, we use a constant learning rate of 1e-3, and we set the temperature parameter β = 1.0 (for smooth approximation of if-then-else primitive) during training. We set the maximum number of epochs as 250, and we use a mini-batch size of 64. |