Empirical Gateaux Derivatives for Causal Inference
Authors: Michael Jordan, Yixin Wang, Angela Zhou
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1a we include an ( , λ)-plot for the simple case of AIPW (see [10] for more discussion and examples). We consider a one-dimensional case with uniformly distributed X, piecewiselinear Y , and smooth propensity scores that are logistic in sin(X). We use n = 500 and fix the bandwidth h = 0.05. Colors denote magnitude of the mean absolute error, included in text on the heatmap. Without loss of generality, we study the estimation of a mean under missingness, E[Y (1)]. Figure 1b illustrates the estimation error of various strategies with the comparable kernel-based estimates (DM is regression adjustment). We include further numerical experiments in the Appendix. |
| Researcher Affiliation | Academia | Michael I. Jordan Department of EECS and Department of Statistics University of California, Berkeley |
| Pseudocode | Yes | Algorithm 1 Empirical Gateaux derivatives |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions generating data for a case study ('one-dimensional case with uniformly distributed X, piecewise-linear Y, and smooth propensity scores'), but it does not specify a publicly available dataset with a citation, link, or repository for access. |
| Dataset Splits | No | The paper describes experimental parameters such as 'n = 500' and a fixed bandwidth, but it does not provide specific details on training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scipy.optimize or r.optim packages' as general examples in a footnote, but it does not list specific software dependencies with version numbers that were used for its own experiments. |
| Experiment Setup | Yes | We use n = 500 and fix the bandwidth h = 0.05. |