Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
Authors: Aaron Fisher
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superior performance of IVWs in simulations |
| Researcher Affiliation | Collaboration | 1Genentech, Boston, MA, United States. Correspondence to: Aaron Fisher <afishe27@alumni.jh.edu>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It describes procedures through mathematical equations and textual explanations. |
| Open Source Code | Yes | Code for reproducing the methods and simulations in this paper is available at https://github.com/aaronjfisher/wpor/. |
| Open Datasets | Yes | We include 6 simulation scenarios, labeled A, B, C, D, E & F. The first four are experiments taken from Nie & Wager (2020), with |X| set equal to 10. Setting E is the low dimensional simulated example from Kennedy (2023). Setting F is the simple illustrative example from Figure 1. ... Table 2 presents each setting in detail, and Table 3 gives a qualitative summary of each setting. |
| Dataset Splits | Yes | For example, for f U,ˆθ, we used 90% of the data to estimate the nuisance functions ˆθ, evaluated and stored f U,ˆθ(Zi) for the remaining 10%, and then repeated this process 10 times with different fold assignments to obtain a pseudo-outcome for every individual. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments or simulations, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'the lightgbm R package written by Shi et al. (2023)' but does not provide specific numerical version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We implemented POR with three pseudo-outcome functions: f U,ˆθ, f DR,ˆθ, and fcov,ˆθ. In each case we used 10-fold crossfitting. ... We used boosted trees to perform all of our nuisance regressions, as well as the final regression predicting pseudo-outcomes as a function of X. ... For each pseudo-outcome function, we considered a weighted and unweighted version. |