Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Authors: Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that our estimator converges to tight bounds on CATE when there may be unobserved confounding and assess it using semi-synthetic, high-dimensional datasets. |
| Researcher Affiliation | Academia | 1OAMTL, University of Oxford 2Machine Learning and Causal Inference in Healthcare Lab, Technion Israel Institute of Technology. |
| Pseudocode | No | The paper describes the steps for computing the interval estimator in Section 3.3, but these steps are presented as descriptive text rather than a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code availability: The code is available at https://github.com/andrewjesson/HiddenConfoundingCATE |
| Open Datasets | Yes | For this experiment, we adopt the one-dimensional simulated setting into a high-dimensional setting C.2. Specifically, we assign to each image of the MNIST dataset (Le Cun, 1998) a latent feature φ [ 2, 2] as follows: all images of the digits 0 are assigned a φ [ 2, 1.6], all images 1 have φ [ 1.6, 1.2], and so on up to the digit 9. [...] To this end we use the IHDP dataset (Hill, 2011) as Jesson et al. (2020) show that low overlap and/or similarity are problems for IHDP. |
| Dataset Splits | Yes | The average and 95% confidence intervals over 50 random realizations of training (n = 1000), validation (n = 100), and test (n = 1000) datasets are reported. |
| Hardware Specification | No | The paper states, 'Details for each experiment, including architectures, hyper-parameter tuning, training procedures, and compute infrastructure are detailed in Appendix D.' However, Appendix D is not provided in the given text, so no specific hardware details are available in the main body. |
| Software Dependencies | No | The paper mentions software like 'Deep Ensembles' and 'Pytorch', but it does not specify any version numbers for these or other software dependencies, which is required for reproducibility. |
| Experiment Setup | No | The paper notes that 'Details for each experiment, including architectures, hyper-parameter tuning, training procedures, and compute infrastructure are detailed in Appendix D.' However, Appendix D is not provided in the given text, thus specific experimental setup details like hyperparameter values are not explicitly stated in the main body. |