Counterfactual Analysis in Dynamic Latent State Models
Authors: Martin B Haugh, Raghav Singal
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply it on a breast cancer case study. We now apply our approach to the breast cancer application we described in 1. The results for path 1 are displayed in Figure 3 (and for path 2 in Figure 10 ( E.5)), where we show the PN bounds as we vary T. |
| Researcher Affiliation | Academia | Martin Haugh 1 Raghav Singal 2 1Imperial College 2Dartmouth College. Correspondence to: MH <m.haugh@imperial.ac.uk>, RS <singal@dartmouth.edu>. |
| Pseudocode | Yes | Algorithm 1 Counterfactual analysis via optimization and Algorithm 2 Counterfactual simulations under the independence copula and Algorithm 3 Counterfactual simulations under the comonotonic copula |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | The primitives (p, E, Q) are calibrated to real-data using a mix of sources, which we discuss in E.1. NIH (2020). URL https://seer.cancer.gov/ archive/csr/1975_2017/results_merged/ sect_04_breast.pdf. UWBCS. University of Wisconsin Breast Cancer Simulation Model. 2013. URL https://resources.cisnet.cancer.gov/ registry/packages/uwbcs-wisconsin/. |
| Dataset Splits | No | The paper describes using "real-data" from external sources to calibrate model primitives, but does not specify train/validation/test dataset splits for its experiments. The mention of "B = 100 samples" refers to Monte Carlo samples, not dataset splits. |
| Hardware Specification | Yes | It solved each of our problem instances to global optimality within minutes / hours (depending on T), with an absolute termination tolerance of 0.01 (on an Intel Xeon E5 processor with 16 GB RAM). |
| Software Dependencies | Yes | We implemented in MATLAB (MATLAB, 2021). The feasibility set F over (θ, π) corresponds to (10), (11), and (12). To solve the polynomial optimizations, we use the MATLAB-BARON interface (Sahinidis, 2023) with CPLEX (IBM, 2017) as the LP / MIP solver. MATLAB. Version 9.10.0 (R2021b). The Math Works Inc., Natick, Massachusetts, 2021. IBM. ILOG CPLEX Optimizer Version 12.8. 2017. Sahinidis, N. V. BARON 2023.1.5: Global Optimization of Mixed-Integer Nonlinear Programs, User s Manual, 2023. |
| Experiment Setup | Yes | We vary T {4, . . . , 10}, with a larger value of T suggesting the cancer may have progressed more slowly. We generated B = 100 samples using our sampling method in B. We ensured this stability by computing our results for 20 seeds (for each (path, T) pair) and verifying the standard deviations to be small. It solved each of our problem instances to global optimality within minutes / hours (depending on T), with an absolute termination tolerance of 0.01 |