Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Authors: Jiaqi Zhang, Kristjan Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now demonstrate our method on a biological dataset. We use the large-scale Perturb-seq study from [44]. After pre-processing, the data contains 8,907 unperturbed cells (observational dataset D) and 99,590 perturbed cells. The perturbed cells underwent CRISPR activation [16] targeting one or two out of 105 genes (interventional datasets D1,...,DK, K = 217). CRISPR activation experiments modulate the expression of their target genes, which we model as a shift intervention. Each interventional dataset comprises 50 to 2,000 cells. Each cell is represented as a 5,000-dimensional vector (observed variable X) measuring the expressions of 5,000 highly variable genes. To test our model, we set the latent dimension p = 105, corresponding to the total number of targeted genes. |
| Researcher Affiliation | Collaboration | Jiaqi Zhang LIDS, MIT Broad Institute of MIT and Harvard Kristjan Greenewald MIT-IBM Watson AI Lab IBM Research Chandler Squires LIDS, MIT Broad Institute of MIT and Harvard Akash Srivastava MIT-IBM Watson AI Lab IBM Research Karthikeyan Shanmugam IBM Research Caroline Uhler LIDS, MIT Broad Institute of MIT and Harvard |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for our method is at https://github.com/uhlerlab/discrepancy_vae. |
| Open Datasets | Yes | We use the large-scale Perturb-seq study from [44]. |
| Dataset Splits | No | The paper mentions reserving samples for testing, but does not explicitly describe a validation dataset split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper mentions training on a “single GPU” but does not specify the model or any other hardware details. |
| Software Dependencies | No | The paper mentions “Py Torch” but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We summarize our hyperparameters in Table 2. ... Loss function Kernel width (MMD) 200 Number of kernels (MMD) 10 λ 0.1 βmax 1 αmax 1 Training tmax 100 Learning rate 0.001 Batch size 32 ... We train for 100 epochs in total, which takes less than 45 minutes on a single GPU. |