Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Estimating individual treatment effect: generalization bounds and algorithms
Authors: Uri Shalit, Fredrik D. Johansson, David Sontag
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 19,846 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,015 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 <EMAIL>, Fredrik D. Johansson <EMAIL>, David Sontag <EMAIL>. |
| 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. |