Regularizing towards Causal Invariance: Linear Models with Proxies
Authors: Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |