ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
Authors: Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using our framework, we build an example method (called INSITE), tested in accepted benchmark settings used throughout the literature. |
| Researcher Affiliation | Academia | Krzysztof Kacprzyk University of Cambridge Samuel Holt University of Cambridge Jeroen Berrevoets University of Cambridge Zhaozhi Qian University of Cambridge Mihaela van der Schaar University of Cambridge |
| Pseudocode | Yes | Algorithm 1 Individualized Nonlinear Sparse Identification Treatment Effect (INSITE) |
| Open Source Code | Yes | We provide all code at https://github.com/samholt/ODE-Discovery-f or-Longitudinal-Heterogeneous-Treatment-Effects-Inference. |
| Open Datasets | Yes | We generate a dataset for each underlying pharmacological model F and a given action policy... This forms a dataset as described in Section 3... Here, to explore continuous types of treatments, we use a continuous chemotherapy treatment c(t) and a binary radiotherapy treatment d(t), both changing over time. For both models y = x, is the volume of the tumor t days after diagnosis, modeled separately as: V x(t), if a = 0 C1 V x(t), if a = 1 (5) dt = ρ log K | {z } Tumorgrowth βc C(t) | {z } Chemotherapy (αrd(t) + βrd(t)2) | {z } Radiotherapy + et |{z} Noise where the parameters C0, C1, V, ρ, K, γ, α, β, et are sampled according to the different layers of between-subject variability (table 2) forming variations of A-D, with parameter distributions following that as described in Geng et al. (2017) or otherwise detailed in appendix F. |
| Dataset Splits | Yes | With 1000 training trajectories, 100 validation trajectories and 100 test trajectories, unless otherwise noted. |
| Hardware Specification | Yes | We perform all experiments and training using a single Intel Core i9-12900K CPU @ 3.20GHz, 64GB RAM with an Nvidia RTX3090 GPU 24GB. |
| Software Dependencies | No | The paper states, 'all the baselines are implemented in Py Torch lightning (Falcon, 2019) and trained with the Adam optimizer (Kingma & Ba, 2014)'. While it names software components and cites papers, it does not provide specific version numbers for PyTorch, Python, or other libraries, which is required for reproducibility. |
| Experiment Setup | Yes | The hyperparameters are: the propensity treatment model has 8 sequential hidden units, a dropout rate of 0.1, one layer, uses a batch size of 64, with a max grad norm of 2.0, and is optimized with the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 0.001... Here the regularization parameter λ is set to λ = 10.0 across all experiments. |