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 [1].
Causal Estimation with Functional Confounders
Authors: Aahlad Puli, Adler Perotte, Rajesh Ranganath
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we prove error bounds on LODE s effect estimates, evaluate our methods on simulated and real data, and empirically demonstrate the value of EFC. We evaluate LODE on simulated data ο¬rst and show that LODE can correct for confounding. We also investigate the error induced by imperfect estimation of the surrogate intervention in LODE. Further, we run LODE on a GWAS dataset [6] and demonstrate that LODE is able to correct for confounding and recovers genetic variations that have been reported relevant to Celiac disease [8, 25, 14, 1]. |
| Researcher Affiliation | Academia | 1Computer Science, New York University, New York, NY 10011 2Biomedical Informatics, Columbia University, New York, NY 10032 3Center for Data Science, New York University, New York, NY 10011 |
| Pseudocode | Yes | See algorithm 1 for a description. |
| Open Source Code | No | The paper does not explicitly state that source code for their methodology is provided or available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We utilize data from the Wellcome Trust Celiac disease GWAS dataset [8, 6] consisting of individuals with celiac disease, called cases (n = 3796), and controls (n = 8154). |
| Dataset Splits | Yes | We use a 60 40% train-test split, and outcome model selection is done via cross-validation within the training data (60% of the dataset). We did 5-fold cross-validation using just the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments, only referring to 'simulated data' and general experimental settings. |
| Software Dependencies | No | The paper mentions using 'Scikit-learn [18]' and 'kernel ridge regression' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Let the dimension of t (pre-outcome variables) be T = 20 and outcome noise be Ξ· N(0, 0.1). We train on 1000 samples and report conditional effect root-mean-squared error (RMSE), computed with another 1000 samples. We used a degree-2 kernel ridge regression to ο¬t the outcome model as a function of t. We use a 60 40% train-test split, and outcome model selection is done via cross-validation within the training data (60% of the dataset). ... The best outcome model was a Lasso model, trained with regularization constant 10. We select relevant SNPs by thresholding estimated effects at a magnitude > 0.1. |