Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
Authors: Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on five benchmarks and show that our method outperforms six state-of-the-art methods on the counterfactual estimation error. 5 Experiments Dataset Following the prior work (Nie et al. 2021; Zhang et al. 2022; Bica, Jordon, and van der Schaar 2020), we use five datasets, namely IHDP, NEWS, and TCGA(0-2) on three types of treatments. Across all datasets, t [0, 1]. Details are deferred to the Appendix. |
| Researcher Affiliation | Academia | Indian Institute of Technology Bombay EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: GIKS training |
| Open Source Code | Yes | We release the code at: https://github.com/nlokeshiisc/GIKS release. |
| Open Datasets | Yes | We use five datasets, namely IHDP, NEWS, and TCGA(0-2) on three types of treatments. Details are deferred to the Appendix. Following the prior work (Nie et al. 2021; Zhang et al. 2022; Bica, Jordon, and van der Schaar 2020) |
| Dataset Splits | Yes | We allocate 30% samples as validation dataset to tune hyperparameters. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Adam W optimizer' and 'cosine kernel' but does not specify version numbers for any software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | GIKS has three hyperparameters: learning rate, λGI, λGP that are optimized via grid search on factual error of the validation dataset. Further, the GP employs a cosine kernel. We use a batch size of 128, the Adam W optimizer, and early stopping based on factual error on the validation dataset. The results of hyperparameter tuning are presented in Table 3. TCGA(0-2) IHDP NEWS lrn rate 10 4 10 2 10 3 λGI 10 1 10 4 10 2 λKS 10 2 10 1 10 4 Table 3: The hyperparameters. |