On Inductive Biases for Heterogeneous Treatment Effect Estimation
Authors: Alicia Curth, Mihaela van der Schaar
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement instantiations of all approaches using NNs and evaluate their performance across a wide range of semi-synthetic experiments. We empirically confirm that all approaches can improve upon baselines, including both end-to-end and multi-stage approaches, and present a number of insights into the relative strengths of each approach. |
| Researcher Affiliation | Academia | Alicia Curth University of Cambridge amc253@cam.ac.uk Mihaela van der Schaar University of Cambridge University of California, Los Angeles The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | Yes | refer to Appendix B.3 for pseudocode of a Flex TENet forward pass. |
| Open Source Code | Yes | Code to replicate all experiments is available at https://github.com/Alicia Curth/CATENets |
| Open Datasets | Yes | For setups A&B, we use the ACIC2016 covariates (n = 4802, d = 55) of [45] but design our own response surfaces... For setups C&D, we use the IHDP benchmark (n = 747, d = 25), into which [1] introduced confounding, imbalance (18% treated) and incomplete overlap. |
| Dataset Splits | No | The paper mentions '90/10 train-test splits' for the IHDP benchmark, but does not explicitly detail a separate validation split or its proportions for reproducibility for all experiments. For setups A&B, it states using '500 units for testing' and varying n0 and n1 for control/treatment, but does not specify a validation set. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing models using 'neural networks (NNs)' and Appendix B.3 provides 'pseudocode' which implies software, but it does not specify any software dependencies with version numbers (e.g., 'PyTorch 1.x', 'Python 3.x'). |
| Experiment Setup | Yes | We implement all neural network models in PyTorch. For all models, we use the Adam optimizer with a learning rate of 1e-3. We train for 100 epochs with a batch size of 256. Early stopping is applied with a patience of 10 epochs. We use ELU activation functions throughout the network. The hidden layers of the representation Φ and regression heads h w consist of 3 and 2 layers, respectively. |