Learning Triggers for Heterogeneous Treatment Effects
Authors: Christopher Tran, Elena Zheleva5183-5190
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on multiple datasets show that our approach can learn the triggers better than existing approaches. |
| Researcher Affiliation | Academia | Christopher Tran, Elena Zheleva Department of Computer Science, University of Illinois at Chicago Chicago, IL, USA {ctran29, ezheleva}@uic.edu |
| Pseudocode | Yes | Algorithm 1 Learning trigger-based causal trees |
| Open Source Code | Yes | Code available: https://github.com/chris-tran-16/CTL |
| Open Datasets | Yes | We study two datasets that lend themselves to the trigger-based treatment problem which is the focus of our work. We also use the ACIC Causal Inference Challenge dataset for binary treatments. For the first dataset, we use data from the 1987 National Medical Expenditure Survey (NMES) (Johnson et al. 2003). The second dataset is a cloud storage usage survey, which asks users various questions about their cloud storage (Khan et al. 2018). The last dataset comes from the ACIC 2018 Causal Inference Challenge2. |
| Dataset Splits | Yes | For each dataset we do an 80-20 randomized split for the training and testing set for evaluation. For ours and honest trees, we split the training into two parts: a training Str and validation Sval. For our approach, we clearly separate a training, validation, and testing sample for training and evaluation. We build the tree on the training portion, while penalizing generalization ability based on the validation set. We learn λ and the validation split size for our methods on a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or specific computing platforms) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific versions of software libraries, frameworks, or programming languages used (e.g., Python version, specific machine learning library versions). |
| Experiment Setup | Yes | For each dataset we do an 80-20 randomized split for the training and testing set for evaluation. For ours and honest trees, we split the training into two parts: a training Str and validation Sval. We grow a maximum sized tree and prune all trees based on statistical significance (α = 0.05). We learn λ and the validation split size for our methods on a separate validation set. |