Scalable Intervention Target Estimation in Linear Models
Authors: Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, simulation results on both real and synthetic data demonstrate the gains of the proposed approach for scalable causal structure recovery. |
| Researcher Affiliation | Collaboration | Burak Varıcı Rensselaer Polytechnic Institute varicb@rpi.edu Karthikeyan Shanmugam IBM Research AI karthikeyan.shanmugam2@ibm.com |
| Pseudocode | Yes | Algorithm 1 Causal Intervention Target Estimator (CITE) Algorithm 2 Functions for the main algorithm |
| Open Source Code | Yes | Implementation of the algorithm and the code to reproduce the simulation results are available at https://github.com/bvarici/intervention-estimation. |
| Open Datasets | Yes | Protein signaling data. We first consider the dataset in [22] for discovering the protein signaling network of 11 nodes. It consists of measurements of proteins and phospolipids under different interventional environments. Perturb-seq gene expression data. We analyze the performance of our algorithm on the perturb-seq dataset by in [24]. |
| Dataset Splits | No | The paper mentions sample sizes (e.g., "5000 samples", "1755 observational and 4091 interventional samples") but does not provide specific training/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | All the simulations are run on a Mac Book Pro with 2.7 GHz Dual-Core i5 core and 8 GB RAM. |
| Software Dependencies | No | The paper mentions using an "ADMM-based method" from [12] but does not provide specific software names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We generate 100 realizations of Erd os-Rényi [21] DAGs with expected neighborhood size c = 1.5, and |I| = 5. We sample the entries of B, i.e., the edge weights, independently at random according to the uniform distribution on [ 1, 0.25] [0.25, 1]. The additive Gaussian noise terms have distribution N(0, Ip). We select the intervention set I by randomly selecting 5 nodes from [p]. We consider three different models to intervene on the nodes in I: (i) shift intervention model in which mean of the noise ϵi is shifted from 0 to 1, (ii) variance increase model in which the variance of the noise ϵi is increased from 1 to 2, and (iii) randomized intervention model, in which (B(2))pa(i),i = 0 and the noise variance varies from 1 to 1.5. |