Learning Causal Models under Independent Changes
Authors: Sarah Mameche, David Kaltenpoth, Jilles Vreeken
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that our method performs well in a range of synthetic settings, on realistic gene expression simulations, as well as on real-world cell signaling data. |
| Researcher Affiliation | Academia | Sarah Mameche CISPA Helmholtz Center for Information Security sarah.mameche@cispa.de David Kaltenpoth CISPA Helmholtz Center for Information Security david.kaltenpoth@cispa.de Jilles Vreeken CISPA Helmholtz Center for Information Security jv@cispa.de |
| Pseudocode | Yes | Algorithm 1: LINC Input: data X(c), candidate DAGs G. Output: DAG G and partitions Π. and Algorithm 2: LINCCLUS Input: data X(c), variable Xi with causes XS. Output: partition Πi. |
| Open Source Code | Yes | We make the code and datasets available in the supplement. |
| Open Datasets | Yes | We make the code and datasets available in the supplement. ... we simulate data with SERGIO [19] to generate single-cell expression data... Finally, we evaluate LINC on real-world data over eleven proteins and phospholipid components... which Sachs et al. [20] added to the system in multiple experiments. |
| Dataset Splits | No | The paper describes generating synthetic data and simulations with specific parameters and sample sizes per context (e.g., "|c| = 500 samples"), but it does not specify explicit train/validation/test dataset splits (e.g., percentages or exact counts for each partition) or reference standard predefined splits. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using specific software tools like "KCI test" but does not provide version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We sample DAGs G of size |G| = 6 with edge density p = 0.3 and generate data in |C| = 5 contexts, with |c| = 500 samples and |S| = 2. ... For the synthetic data, following the literature [4, 13] we generate data in multiple contexts using ω(c) ij fij(X(c) i ) + σ(c) j N (c) j , (8) with weights ω(c) ij U(0.5, 2.5), where noise is either uniform or Gaussian with equal probability. We sample the causal functions f from {x2, x3, tanh, sinc}. |