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..
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Authors: Liangyu Zhu, Wenbin Lu, Rui Song
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation studies and an application to the Ohio type 1 diabetes dataset show that our method could provide meaningful insights for dose suggestions with mobile health data. 4. Simulation Studies, 5. Type 1 Diabetes Data Analysis |
| Researcher Affiliation | Academia | 1Department of Statistics, North Carolina State University, Raleigh, NC, USA. Correspondence to: Liangyu Zhu <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The R code for the simulation can be found in https:// github.com/lz2379/Mhealth. The R code for real data application can be found in https: //github.com/lz2379/Mhealth. |
| Open Datasets | Yes | We apply our method to the Ohio type 1 diabetes dataset collected by Marling & Bunescu (2018)... Marling, C. and Bunescu, R. C. The ohiot1dm dataset for blood glucose level prediction. In KHD@ IJCAI, pp. 60 63, 2018. |
| Dataset Splits | No | The paper only specifies a training and testing split: "We further take the first 44 days as the training data and the last 10 days as the testing data." There is no explicit mention of a validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions that the code is in R ("The R code for the simulation", "The R code for real data application") but does not specify any libraries or their version numbers. |
| Experiment Setup | Yes | We take σ = 0.5, θ1 = 0.8, θ2 = 0, η1 = 0.2, η2 = 0.2, τ1 = 1, τ2 = 0.5, β0 = 0, β1 = 2 and St = Xt. We use the Gaussian kernel KΛ(s) = (2π) q/2|Λ| 1/2 exp( s T Λs/2), where q = 1 is the dimension of St, and f(St) = St. ... Λ is a q q diagonal matrix with Λj,j = λ2 j. We take λj = 0.305 n 1/3sd(St,j), j = 1, . . . , q. |