$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
Authors: Pengzhou Abel Wu, Kenji Fukumizu
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is compared with recent methods using (semi-)synthetic datasets. Our main contributions are: 4) experimental comparison to the state-of-the-art methods on (semi-)synthetic datasets. |
| Researcher Affiliation | Academia | Pengzhou (Abel) Wu & Kenji Fukumizu Department of Statistical Science, The Graduate University for Advanced Studies & The Institute of Statistical Mathematics Tachikawa, Tokyo {wu.pengzhou,fukumizu}@ism.ac.jp |
| Pseudocode | No | The paper describes its methods through prose and mathematical equations but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | The code is in Supplementary Material, and the proofs are in Sec. A. |
| Open Datasets | Yes | We compare our method with existing methods on three types of datasets. Here, we present two experiments; the remaining one on the Pokec dataset is deferred to Sec. E.3. IHDP (Hill, 2011) is a widely used benchmark dataset. Pokec (Leskovec & Krevl, 2014) is a real world social network dataset. |
| Dataset Splits | Yes | For each DGP, we sample 1500 data points, and split them into 3 equal sets for training, validation, and testing. We split the dataset by 63:27:10 for training, validation, and testing. |
| Hardware Specification | No | The paper mentions training models using an Adam optimizer and multilayer perceptrons but does not specify any particular hardware (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" and "ELU activations" but does not provide specific version numbers for any software libraries, frameworks (like PyTorch or TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Unless otherwise indicated, for each function f, g, h, k, r, s in ELBO (7), we use a multilayer perceptron, with 200 3 hidden units (width 200, 3 layers), and ELU activations (Clevert et al., 2015). Λ = (h, k) depends only on X. The Adam optimizer with initial learning rate 10 4 and batch size 100 is employed. All experiments use early-stopping of training by evaluating the ELBO on a validation set. |