Estimating individual treatment effect: generalization bounds and algorithms
Authors: Uri Shalit, Fredrik D. Johansson, David Sontag
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art. |
| Researcher Affiliation | Academia | 1CIMS, New York University, New York, NY 10003 2IMES, MIT, Cambridge, MA 02142 3CSAIL, MIT, Cambridge, MA 02139. Correspondence to: Uri Shalit <shalit@cs.nyu.edu>, Fredrik D. Johansson <fredrikj@mit.edu>, David Sontag <dsontag@csail.mit.edu>. |
| Pseudocode | Yes | Algorithm 1 CFR: Counterfactual regression with integral probability metrics |
| Open Source Code | Yes | Both versions are implemented3 as feed-forward neural networks...3https://github.com/clinicalml/cfrnet |
| Open Datasets | Yes | Hill (2011) compiled a dataset for causal effect estimation based on the Infant Health and Development Program (IHDP)... The study by La Londe (1986) is a widely used benchmark in the causal inference community... |
| Dataset Splits | Yes | We average over 1000 realizations of the outcomes with 63/27/10 train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., CPU, GPU models, or memory). |
| Software Dependencies | No | The paper mentions software like 'Adam (Kingma & Ba, 2015)' and 'NPCI package (Dorie, 2016)' but does not specify version numbers for any libraries or frameworks used in their implementation. |
| Experiment Setup | Yes | Layer sizes were 200 for all layers used for Jobs and 200 and 100 for the representation and hypothesis used for IHDP. The model is trained using Adam (Kingma & Ba, 2015). The hypothesis parameters are regularized with a small ℓ2 weight decay. For continuous data we use mean squared loss and for binary data, we use log-loss. |