Conditional Outcome Equivalence: A Quantile Alternative to CATE
Authors: Josh Givens, Henry Reeve, Song Liu, Katarzyna Reluga
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theory in numerical simulations which show that our method produces more accurate estimates than baselines. Finally, we apply our methodology to a study on the effect of employment incentives on earnings across different age groups. |
| Researcher Affiliation | Academia | Josh Givens University of Bristol josh.givens@bristol.ac.uk Henry W J Reeve University of Bristol henry.reeve@bristol.ac.uk Song Liu University of Bristol song.liu@bristol.ac.uk Katarzyna Reluga University of Bristol katarzyna.reluga@bristol.ac.uk |
| Pseudocode | Yes | Algorithm 1 DR estimation procedure for the CCDF contrasting function h", "Algorithm 2 DR estimation procedure for the CQC g |
| Open Source Code | Yes | Code implementation can be found at: github.com/joshgivens/Conditional Outcome Equivalence", "Code to implement our approach alongside Jupyter notebooks running our numerical experiments can be found in the supplementary materials. |
| Open Datasets | Yes | We use a dataset on an employment programme which has been studied in various prior works [4, 5, 26].", "This dataset was originally introduced in Laurie et al. [19] and can be found in the survival" package in R and loaded with the line data(colon, package="survival"). |
| Dataset Splits | No | In our experiments half the samples are used to estimate the propensity score and CCDFs and the other half are used to regress against the pseudo-outcome. |
| Hardware Specification | Yes | Each experiment took no longer than 1 hour to run on a single 4 core CPU with 8GB of RAM. |
| Software Dependencies | No | We can use the Pool Adjacent Violators Algorithm (PAVA) which performs isotonic projection and is implemented in the Isotonic Regression class of sci-kit learn in Python [25]. |
| Experiment Setup | Yes | In our experiments half the samples are used to estimate the propensity score and CCDFs and the other half are used to regress against the pseudo-outcome. In our first experiment, we let 2n = 1000 and vary γ in [0, 10]. In our second experiment, we let γ = 6 and 2n vary in [200, 5000]. |