Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Authors: Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate the ability of TE-CDE to estimate counterfactual outcomes from irregularly sampled observational data. [...] TE-CDE consistently outperforms existing approaches in all simulated scenarios with irregular sampling. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK 2University of California, Los Angeles, USA 3Columbia University, USA 4The Alan Turing Institute, London, UK. |
| Pseudocode | No | The paper describes the methods textually and mathematically but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://github.com/seedatnabeel/TE-CDE and https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/TE-CDE |
| Open Datasets | Yes | First, we describe a simulation environment based on a Pharmacokinetic Pharmacodynamic (PK-PD) model of lung cancer tumor growth (Geng et al., 2017), which allows counterfactuals to be calculated at any time point for arbitrary treatment plans. [...] We consider four treatment options: no treatment, chemotherapy, radiotherapy, and combined chemotherapy and radiotherapy. The assignment of chemotherapy and radiotherapy are modeled as Bernoulli random variables with probabilities pc and pr, respectively, that depend on tumor diameter3 as follows: pc(t) = σ γc Dmax D(t) θc , pr(t) = σ γr Dmax D(t) θr , where Dmax = 13cm is the maximum tumor diameter, θc = θr = Dmax/2 and D(t) is the average tumor diameter. |
| Dataset Splits | Yes | Unless otherwise stated, each experiment is run with 10,000 patients for training, 1,000 for validation and 10,000 for testing. |
| Hardware Specification | Yes | The model was implemented with Pytorch and Torch CDE and was trained and evaluated on a single Nvidia P100 or T4 GPU. |
| Software Dependencies | No | The paper mentions PyTorch, torchcde, torchdiffeq, and the Dormand Prince (dopri5) solver, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The integrand of the neural CDE (i.e. f) is a 2-layer neural network with hidden states of size=128. The dimensionality of the latent state z is 8. We use linear interpolation when defining the control path Xt. [...] For domain adversarial training, we use the standard procedure (Ganin et al., 2016), with an initial µ = 0 that follows an exponentially increasing schedule per epoch of training for the range [0, 1]. [...] Both encoder and decoder are trained for 100 epochs each. That said we also include early stopping in the training protocol based on the validation loss, with patience=5. When MC Dropout is included, we use a dropout probability=0.1. [...] We did tune the learning rate (lr = 1e-3, 1e-4 , 1e-5, 1e-6) based on performance on the validation set. |