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 [1].
Classifying Treatment Responders Under Causal Effect Monotonicity
Authors: Nathan Kallus
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks. |
| Researcher Affiliation | Academia | Nathan Kallus 1 School of Operations Research and Information Engineering and Cornell Tech, Cornell University. Correspondence to: Nathan Kallus <EMAIL>. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks. We next study the application of our methods to the data derived from 1980 census. Following Angrist & Evans (1996), we construct a dataset of married couples with at least two children. |
| Dataset Splits | Yes | Resp SVM with linear kernel and 5-fold cross validation (CV) to choose regularization (with Lθ for scoring) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "Keras and Tensor Flow" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Resp Nets and TARNets are implemented using Keras and Tensor Flow and trained with Adam for 100 epochs. |