Estimating Causal Effects Using Weighting-Based Estimators
Authors: Yonghan Jung, Jin Tian, Elias Bareinboim10186-10193
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We derive graphical criteria under which causal effects can be estimated using this new machinery and demonstrate the effectiveness of the proposed method through simulation studies. ... Section 6 Simulation Studies |
| Researcher Affiliation | Academia | Yonghan Jung Department of Computer Science Purdue University jung222@purdue.edu; Jin Tian Department of Computer Science Iowa State University jtian@iastate.edu; Elias Bareinboim Department of Computer Science Columbia University eb@cs.columbia.edu |
| Pseudocode | No | The paper describes methods verbally and with mathematical formulas but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any link or explicit statement about releasing the source code for the methodology described. |
| Open Datasets | No | The paper states: 'Given a causal graph, we will specify a SCM M from which a dataset Dobs will be generated.' This indicates synthetic data generation, but no access information (link, citation, repository) for a publicly available or open dataset is provided. |
| Dataset Splits | No | The paper performs simulations but does not describe specific training, validation, or test splits by percentages, absolute counts, or reference to standard splits. |
| Hardware Specification | No | The paper describes simulation studies but does not mention any specific hardware used (e.g., GPU models, CPU types, cloud instances). |
| Software Dependencies | No | The paper mentions 'logistic regression model' but does not provide specific software names with version numbers (e.g., Python 3.x, scikit-learn x.x.x). |
| Experiment Setup | No | The paper describes the simulation setup (e.g., binary variables for Z, continuous for Y) but does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings for the empirical estimations. |