Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
Authors: Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 nlokesh@cse.iitb.ac.in, akshaygiyer@gmail.com, abir@cse.iitb.ac.in, sunita@iitb.ac.in |
| 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. |