Counterfactual Temporal Point Processes
Authors: Kimia Noorbakhsh, Manuel Rodriguez
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions. |
| Researcher Affiliation | Academia | Kimia Noorbakhsh Sharif University of Technology kimianoorbakhsh@gmail.com Manuel Gomez Rodriguez Max Planck Institute for Software Systems manuelgr@mpi-sws.org |
| Pseudocode | Yes | Algorithm 1 It samples a counterfactual sequence of accepted events given a sequence of accepted and rejected events provided by Lewis thinning algorithm |
| Open Source Code | Yes | To facilitate research in this area, we release an open-source implementation of our algorithms and data at https://github.com/NetworksLearning/counterfactual-ttp. |
| Open Datasets | Yes | fitted using real event data from an Ebola outbreak in West Africa in 2013-2016 [52]. |
| Dataset Splits | No | The paper describes generating synthetic data or sampling realizations from fitted models and then sampling counterfactual realizations, but it does not specify explicit train/validation/test dataset splits for reproducibility. |
| Hardware Specification | Yes | All experiments were performed on a machine with 48 Intel(R) Xeon(R) 3.00GHz CPU cores and 1.5TB. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, or CUDA versions) in the main text or the included checklist. |
| Experiment Setup | Yes | In each experiment, we first sample 1,000 realizations from a process with one set of parameters using Algorithm 4 (or Algorithm 5). Then, we carry out the above mentioned intervention and, for each of the sampled realizations, we use Algorithm 2 (or Algorithm 3) to sample 100 counterfactual realizations under the resulting alternative set of parameters. ... In all experiments, Algorithms 1 3 use 100 samples from the posterior distribution P C | Xi=x,Λi=λ(ti) ; do(Λi=λm (ti))(Ui) of each Gumbel noise variable Ui,x to estimate the counterfactual thinning probabilities P C | Xi=x,Λi=λ(ti) ; do(Λi=λm (ti))(Xi). |