Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
Authors: Karren Yang, Abigail Katcoff, Caroline Uhler
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets. |
| Researcher Affiliation | Academia | Massachusetts Institute of Technology, Cambridge, MA. Correspondence to: Caroline Uhler <cuhler@mit.edu>. |
| Pseudocode | Yes | Algorithm 1 IGSP for general interventions |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the methodology described. |
| Open Datasets | Yes | Protein Expression Dataset: We evaluated our algorithm on the task of learning a protein network from a protein mass spectroscopy dataset (Sachs et al., 2005). Gene Expression Dataset: We also evaluated IGSP on a single-cell gene expression dataset (Dixit et al., 2016). |
| Dataset Splits | No | The paper describes using datasets for evaluation, but does not explicitly provide specific train/validation/test dataset splits, percentages, or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For each simulation, we sampled 100 DAGs from an Erd os Renyi random graph model with an average neighborhood size of 1.5 and p {10, 20} nodes. The data for each causal DAG G was generated using a linear structural equation model with independent Gaussian noise: X = AX + ϵ, where A is an upper-triangular matrix with edge weights Aij = 0 if and only if i j, and ϵ N(0, Ip). For Aij = 0, the edge weights were sampled uniformly from [ 1, 0.25] [0.25, 1]. We simulated perfect interventions on i by setting the column A,i = 0; inhibiting interventions by decreasing A,i by a factor of 10; and imperfect interventions with a success rate of α = 0.5. |