Causal Effect of Functional Treatment
Authors: Ruoxu Tan, Wei Huang, Zheng Zhang, Guosheng Yin
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study their theoretical properties, which are corroborated through extensive numerical experiments. A real data application on electroencephalography data and disease severity demonstrates the practical value of our methods. Simulation experiments and the real data analysis are presented in Sections 6 and 7, respectively. |
| Researcher Affiliation | Academia | Ruoxu Tan EMAIL School of Mathematical Sciences and School of Economics and Management Tongji University Shanghai, China; Wei Huang EMAIL School of Mathematics and Statistics University of Melbourne Melbourne, VIC, Australia; Zheng Zhang EMAIL Center for Applied Statistics, Institute of Statistics & Big Data Renmin University of China Beijing, China; Guosheng Yin EMAIL Department of Statistics and Actuarial Science University of Hong Kong Hong Kong SAR, China |
| Pseudocode | No | The paper describes methods verbally and mathematically (e.g., the backfitting algorithm for OR estimators), but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code to reproduce the simulation results for our methods and the FLR is available at https://github.com/ruoxut/FunctionalTreatment. |
| Open Datasets | Yes | We illustrate the estimation of the ADRF using the five methods, FSW, OR, DR, PCW and FLR, on the electroencephalography (EEG) dataset from Ciarleglio et al. (2022). |
| Dataset Splits | Yes | In the first stage, we select the truncation parameter q using the OR estimator. Specifically, we randomly split the dataset into L parts, S1, . . . , SL. Let S ℓdenote the remaining sample with Sℓexcluded. |
| Hardware Specification | Yes | The experiments were carried out on a PC with an i7-12700 CPU and 16 GB RAM. |
| Software Dependencies | No | The paper mentions that code is available on GitHub but does not explicitly state specific software dependencies with version numbers, such as Python 3.8 or PyTorch 1.9. |
| Experiment Setup | Yes | We select the tuning parameters h, k and q following the procedure in Section 5, and choose the number of PCs used in estimating the PCW so as to explain 95% of the variance of Z following Zhang et al. (2021). For a fair comparison, we use the truncation parameter bq CV in Section 5 for estimating b in all methods that utilize the functional linear model. We consider the sample sizes n = 200 and 500, and generate data for models (i) to (v) as follows. where ϵi1 N(0, 1) and ϵi2 N(0, 25) are generated independently. |