Domain adaptation under structural causal models
Authors: Yuansi Chen, Peter Bühlmann
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement the theoretical analysis with extensive simulations to show the necessity of the devised assumptions. Reproducible synthetic and real data experiments are also provided to illustrate the strengths and weaknesses of DA methods when parts of the assumptions in our theory are violated. |
| Researcher Affiliation | Academia | Yuansi Chen EMAIL Peter B uhlmann EMAIL Seminar for Statistics ETH Z urich Z urich, Switzerland |
| Pseudocode | No | The paper describes methods through mathematical formulations and prose, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code to reproduce all the numerical experiments is publicly available in the Github repository https://github.com/yuachen/Causal DA. |
| Open Datasets | Yes | MNIST dataset (Le Cun, 1998)... Amazon Review Data (Ni et al., 2019). |
| Dataset Splits | Yes | For each experiment, we take random 20% of samples from the source dataset as the source environment and random 20% of samples from the target dataset as the target environment. ... In all experiments in this subsection, we use large sample size n = 5000 for each environment, n = 5000 target test data for the evaluation of the target risk |
| Hardware Specification | No | The paper mentions software for experiments (Pytorch, Adam's optimizer, SGD), but does not specify any hardware components like GPU or CPU models, or cloud computing instance types. |
| Software Dependencies | No | All the methods in this subsection are implemented via Pytorch (Paszke et al., 2019) and are optimized with Pytorch s stochastic gradient descent. Specifically, stochastic gradient descent (SGD) optimizer is used with step-size (or learning rate) 10 4, batchsize 500 and number of epochs 100. While PyTorch is mentioned, a specific version number for the software dependency is not provided. |
| Experiment Setup | Yes | In all experiments in this subsection, we use large sample size n = 5000 for each environment, n = 5000 target test data for the evaluation of the target risk and we fix the regularization parameters λmatch = 10.0 and λCIP = 1.0 to focus the discussions on the choice of DA estimators. ... All the methods in this subsection are implemented via Pytorch (Paszke et al., 2019) and are optimized with Pytorch s stochastic gradient descent. Specifically, stochastic gradient descent (SGD) optimizer is used with step-size (or learning rate) 10 4, batchsize 500 and number of epochs 100. |