Generative Intervention Models for Causal Perturbation Modeling
Authors: Nora Schneider, Lars Lorch, Niki Kilbertus, Bernhard Schölkopf, Andreas Krause
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On synthetic data and sc RNA-seq drug perturbation data, GIMs achieve robust out-of-distribution predictions on par with unstructured approaches, while effectively inferring the underlying perturbation mechanisms, often better than other causal inference methods. |
| Researcher Affiliation | Academia | 1Technical University of Munich and Helmholtz Munich, Germany 2Department of Computer Science, ETH Zurich, Switzerland 3Munich Center for Machine Learning, Germany 4MPI for Intelligent Systems, T ubingen, Germany 5ELLIS Institute T ubingen, Germany. Correspondence to: Nora Schneider <EMAIL>. |
| Pseudocode | No | The paper describes methods and derivations mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Nora Schneider/gim |
| Open Datasets | Yes | Sci Plex3 drug perturbation data We also evaluate the predictive performance of GIMs on sc RNA-seq data by Srivatsan et al. (2020). |
| Dataset Splits | Yes | The training dataset consists of a total of 160 perturbational datasets (corresponding to 40 Hill functions considered at 4 different dosages), where each one has nk = 50 samples, and one observational dataset with n0 = 800 samples. The partially out-of-distribution test dataset consists of 200 perturbational contexts corresponding to the same 40 hill functions and intervention targets from the training dataset, but evaluated at 5 different dosages, c {0.25, 0.75, 1.25, 1.75, 2.25}. Finally, the fully out-of-distribution test dataset consists of 80 perturbational contexts corresponding to 20 newly sampled hill functions, which are evaluated at dosages c {0.5, 1, 1.5, 2}. ... For evaluation, we create test sets by holding out, one at a time, the highest dosage (10µM) of each drug, resulting in four unique training-test splits per cell type. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions several software components and methods (e.g., Adam optimization, Gumbel-sigmoid, NO-BEARS, OTT library) but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We employ gradient-based optimization to obtain MAP estimates for Mα and ϕ. In Appendix B.2 we provided the gradients of the posterior allowing us to use Adam optimization (Kingma & Ba, 2015) with a learning rate of 0.001. On synthetic data, we use 30000 steps and on drug perturbation data, we use 100000 steps. For the Monte Carlo approximations, we use a sample size of n MC = 128. We apply a cosine annealing schedule to the coefficient, βI, which controls the sparsity of the intervention targets. ... Table 1 and Table 2 list hyperparameter search ranges for the experiments. |