Reconsidering Generative Objectives For Counterfactual Reasoning
Authors: Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider a wide range of semi-synthetic and real-world tasks to validate our models experimentally. Details of the experimental setup are described the SM, and our code is available from https: //github.com/Dannie Lu/BV-NICE. Importantly, we want to experimentally unveil aspects that are important for the design of generative causal models. More analyses can be found in the SM. |
| Researcher Affiliation | Academia | Danni Lu1, , Chenyang Tao2, , Junya Chen2,3, Fan Li4, Feng Guo1,5, Lawrence Carin2 1 Department of Statistics, Virginia Tech, Blacksburg, VA, USA 2 Electrical & Computer Engineering, Duke University, Durham, NC, USA 3 School of Mathematical Sciences, Fudan University, Shanghai, China 4 Department of Statistical Science, Duke University, Durham, NC, USA 5 Virginia Tech Transportation Institute, Blacksburg, VA, USA |
| Pseudocode | Yes | Algorithm 1 BV-NICE |
| Open Source Code | Yes | our code is available from https: //github.com/Dannie Lu/BV-NICE. |
| Open Datasets | Yes | Datasets To extensively validate the proposed procedure in a realistic setup, we consider the following four datasets: (i) IHDP1000 [31]: a semi-synthetic dataset with 1, 000 simulations of different treatment and outcomes mechanism. (ii) ACIC2016 [20]: a benchmark dataset released by Atlantic Causal Inference Competition, which involves 77 semi-synthetic datasets with 100 replications each. (iii) JOBS [44]: a real-world dataset with binary outcomes, a small portion of the data comes from randomized trials. (iv) SHRP2 [27]: a 3-year case-cohort study of driver behavior and environmental factors at the onset of crashes and under normal driving conditions, derived from over 1 million hours of continuous video recordings. Detailed descriptions of these datasets can be found in the SM. |
| Dataset Splits | Yes | For practical cross-validation, we use 7/3 split for training and validation respectively, and rely on validation outcome RMSE to set best configuration. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | Model architecture, hyper-parameter tuning and data pre-processing For all instantiations, we use fully-connected multi-layer perceptrons (MLP) as our flexible learner. We randomly sample model architectures (number of layers, hidden units) and other hyper-parameters (learning rate, batch-size, regularization strength, etc.). For practical cross-validation, we use 7/3 split for training and validation respectively, and rely on validation outcome RMSE to set best configuration. |