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..
Regularizing towards Causal Invariance: Linear Models with Proxies
Authors: Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our theoretical findings in synthetic experiments and using real data of hourly pollution levels across several cities in China. |
| Researcher Affiliation | Academia | 1EECS, MIT, Cambridge, USA 2Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for experiments is available at https://github. com/clinicalml/proxy-anchor-regression. |
| Open Datasets | Yes | We test our approach on a real-world heterogeneous dataset of hourly pollution readings in five cities in China, taken over several years (Liang et al., 2016) |
| Dataset Splits | Yes | For Proxy and Cross-Proxy AR (PAR, x PAR, see Section 3), we choose λ [0, 40] by leave-one-group-out cross-validation on the three training seasons, using the first year (2013) of data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We simulate training data (at different levels of signal-to-variance) from an SCM with the structure given in Figure 2, fix λ := 5 and fit PAR and x PAR. For Proxy and Cross-Proxy AR (PAR, x PAR, see Section 3), we choose λ [0, 40] by leave-one-group-out cross-validation on the three training seasons, using the first year (2013) of data. |