Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DRCFS: Doubly Robust Causal Feature Selection
Authors: Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments across a wide range of simulated and semisynthetic datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford 2Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology 3KTH Royal Institute of Technology 4TU Munich 5Helmholtz Munich. |
| Pseudocode | Yes | Algorithm 1 Doubly Robust Causal Feature Selection Algorithm (DRCFS) |
| Open Source Code | No | The paper mentions external R packages used for baselines but does not provide a specific repository link or an explicit statement about the availability of the source code for their proposed method (DRCFS). |
| Open Datasets | Yes | In this part, to assess the performance of our algorithm with a taste of real-world application, we conduct a semi-synthetic experiment based on microbiome abundance data in plant leaves from Regalado et al. (2020). |
| Dataset Splits | Yes | split the n samples D = {(x1i, . . . , xmi, yi)}i=1,...,n into k disjoint sets D1, . . . , Dk; define Dc l D \ Dl for all l [k]; |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'R Packages "Compare Causal Networks"' and '"selective Inference: Tools for Post-Selection Inference"' for baselines, and 'Forest Riesz' for estimation, but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | Algorithm 1 Doubly Robust Causal Feature Selection Algorithm (DRCFS)... split the n samples D = {(x1i, . . . , xmi, yi)}i=1,...,n into k disjoint sets D1, . . . , Dk; |