Learning Causal Effects via Weighted Empirical Risk Minimization
Authors: Yonghan Jung, Jin Tian, Elias Bareinboim
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We consider the following two practical examples shown in Fig. 2, in addition to Example 1. The derivation of target causal effects as weighted distributions by Algo. 1 is provided in Appendix A. 5.2 Experimental Results We evaluate the proposed WERM learning framework against the plug-in estimators in Examples (1,2,3). All variables are binary except that W is set to be a vector of D binary variables to represent high-dimensional covariates. The detailed description of the corresponding SCMs are provided in Appendix D. |
| Researcher Affiliation | Academia | Yonghan Jung Department of Computer Science Purdue University University West Lafayette, IN 47907 jung222@purdue.edu Jin Tian Department of Computer Science Iowa State University Ames, IA 50011 jtian@iastate.edu Elias Bareinboim Department of Computer Science Columbia University New York, NY 10027 eb@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1: w ID (x, y, G, P) Algorithm 2: WERM-ID-R(D, G, x, y) |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We specify a SCM M for each causal graph and generate datasets D from M. This indicates synthetic data generation rather than the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating datasets from SCMs and generating 'mint = 10^7 samples Dint from Mx' to estimate ground truth, but it does not specify explicit training, validation, and test dataset splits or percentages for the generated data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'gradient boosting regression models' and cites 'xgboost' as an example, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions setting H and HW as 'the gradient boosting regression classes' and the use of 'cross-entropy loss function' but does not provide specific hyperparameter values or detailed training configurations required for replication. |