Nonparametric Identifiability of Causal Representations from Unknown Interventions
Authors: Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David Blei, Bernhard Schölkopf
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We sketch possible learning objectives ( 5), and empirically investigate training different generative models ( 6), finding that only those based on the correct causal structure attain the best fit and identify the ground truth. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2Department of Engineering, University of Cambridge, United Kingdom 3Columbia University, USA |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce our experiments is available at: https://github.com/akekic/causal-component-analysis. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: "Synthetic Data Generating Process. We consider linear Gaussian latent SCMs of the form V1 := U1, V2 := αV1 + U2... We generate different latent SCMs by drawing α uniformly from [ 10, 2] [2, 10]... We generate the corresponding mixing functions by uniformly sampling each element of the weight matrices..." |
| Dataset Splits | Yes | We split the dataset into 70% for training, and 15% for validation and held-out test data, each sampled randomly across all environments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper mentions using specific software components like "normalizing flows", "Neural Spline Flows", and "ADAM optimizer" but does not specify their version numbers. |
| Experiment Setup | Yes | We use Neural Spline Flows [30] for the invertible transformation, with a 3-layer feedforward neural network with hidden dimension 128 and permutation in each flow layer and L = 12 layers... Each environment comprises a total of 200k data points. We use the ADAM optimizer [67] with cosine annealing learning rate scheduling, starting with a learning rate of 5 10 3 and ending with 1 10 7. We train the model for 200 epochs with a batch size of 4096. |