CounterFactual Regression with Importance Sampling Weights
Authors: Negar Hassanpour, Russell Greiner
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on two publicly available benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art. 4 Experiments. Table 1: ENo RMSE, PEHE, and ϵATE performance measures (lower is better), each of the form mean (standard deviation) on the IHDP benchmark. Table 2: Aggregated ENo RMSE (lower is better) on the ACIC 18 benchmark. |
| Researcher Affiliation | Academia | Negar Hassanpour and Russell Greiner Department of Computing Science, University of Alberta, Canada {hassanpo, rgreiner}@ualberta.ca |
| Pseudocode | Yes | Algorithm 1 CFR-ISW: Counter Factual Regression with Importance Sampling Weights |
| Open Source Code | No | The paper mentions in the Acknowledgements: 'We wish to thank Dr. Martha White and Junfeng Wen for fruitful conversations, and Dr. Fredrik Johansson for publishing/maintaining the code-base for the CFR method online.' This refers to the code for the CFR method (a baseline), not the code for the authors' proposed CFR-ISW method. |
| Open Datasets | Yes | To make performance comparison easier, however, we do not synthesize our own datasets here. Instead, we use two publicly available benchmarks see Sec. 4.3. Infant Health and Development Program (IHDP) [...] We worked with the same dataset provided by and used in [Shalit et al., 2017; Johansson et al., 2016; Johansson et al., 2018]. Atlantic Causal Inference Conference 2018 (ACIC 18) [...] covariates matrix for each of these datasets are sub-sampled from a covariates table of real-world medical measurements taken from the Linked Birth and Infant Death Data (LBIDD) [Mac Dorman and Atkinson, 1998]. |
| Dataset Splits | Yes | We report the methods performances by averaging over 100 realizations of outcomes with 63/27/10 train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments (e.g., CPU, GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions 'gradient descent optimizer', 'Adam optimizer', 'elu as the non-linear activation function', and 'Maximum Mean Discrepancy (MMD)' but does not provide specific version numbers for any software libraries, frameworks, or tools used (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Hyperparameter Selection. Table 3: Hyperparameters and ranges (e.g., Imbalance parameter α {1E{-2, -1, 0, 1}}, Num. of representation layers {3, 5}, Batch size {100, 300}). We trained CFR-ISW s π0 logistic regression function with gradient descent optimizer and a learning rate of 1E-3. For both CFR and CFR-ISW, we trained the Φ and ht networks with regularization coefficient λ=1E-3, elu as the non-linear activation function, Adam optimizer [Kingma and Ba, 2015], learning rate of 1E-3, and maximum number of iterations of 3000. |