Direct Estimation of Differences in Causal Graphs
Authors: Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the properties of our method through a simulation study and apply it to the analysis of gene expression data from ovarian cancer and during T-cell activation. |
| Researcher Affiliation | Academia | Yuhao Wang Lab for Information & Decision Systems and Institute for Data, Systems and Society Massachusetts Institute of Technology Cambridge, MA 02139 yuhaow@mit.edu |
| Pseudocode | Yes | Algorithm 1 Difference Causal Inference (DCI) algorithm |
| Open Source Code | Yes | The code utilized for the following experiments can be found at https://github.com/csquires/dci. |
| Open Datasets | Yes | We tested our method on an ovarian cancer data set [37] that contains two groups of patients with different survival rates and was previously analyzed using the DPM algorithm in the undirected setting [43]. We applied DCI to single-cell gene expression data of naive and activated T cells in order to study the pathways involved during the immune response to a pathogen. We analyzed data from 377 activated and 298 naive T cells obtained by [34] using the recent drop-seq technology. |
| Dataset Splits | Yes | We initiated DCI using KLIEP, thresholding the edge weights at 0.005, and ran DCI for different tuning parameters and with cross-validation to obtain the final DCI output shown in Figure 2 (c) using stability selection as described in [21]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using KLIEP, stability selection, PC, and GES algorithms but does not provide specific version numbers for these software components or any underlying libraries/frameworks. |
| Experiment Setup | Yes | In Figure 1 we analyzed how the performance of the DCI algorithm changes over different choices of significance levels α. The simulations were performed on graphs with p = 10 nodes, neighborhood size of s = 3 and sample size n {103, 104}. |