Qi Wu, Yijun Li, Yiyan Huang, Hang Cheuk, Leung, Siyi Wang
We lifted 10 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yiyhuang3/CATE_estimator_selection | canonical | 6 of 9 |
| yiyhuang3/cate_estimator_selection | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| estimate_V | Ran | yiyhuang3/CATE_estimator_selection/KL_scorer.py pointer only (licence: NONE) · get_code("c0b7de9f060ca771") |
| get_one_data_set | Ran | yiyhuang3/CATE_estimator_selection/util.py pointer only (licence: NONE) · get_code("91ab40c5f23aebeb") |
| kl_nn | Ran | yiyhuang3/cate_estimator_selection/KL_scorer.py pointer only (licence: NONE) · get_code("14ff1a69483300bd") |
| load_data | Ran | yiyhuang3/CATE_estimator_selection/main_experiment.py pointer only (licence: NONE) · get_code("a8e7bea8c80668de") |
| load_data_npz | Ran | yiyhuang3/CATE_estimator_selection/util.py pointer only (licence: NONE) · get_code("9870ae099452182f") |
| net_loss | Ran | yiyhuang3/CATE_estimator_selection/method.py pointer only (licence: NONE) · get_code("b1e316d75fbfd576") |
| prepare_ihdp_data | Ran | yiyhuang3/CATE_estimator_selection/util.py pointer only (licence: NONE) · get_code("f51d3c9f5f14275f") |
| generate_inner | Not yet run | yiyhuang3/CATE_estimator_selection/ACIC_generate.py pointer only (licence: NONE) · get_code("15d09a2a3ca8871f") |
| get_acic_covariates | Not yet run | yiyhuang3/CATE_estimator_selection/ACIC_generate.py pointer only (licence: NONE) · get_code("2cea308a9e5bdb95") |
| sigmoid | Not yet run | yiyhuang3/CATE_estimator_selection/ACIC_generate.py pointer only (licence: NONE) · get_code("56d252c766b19ec0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE estimator selection, such as plug-in and pseudo-outcome metrics, face two challenges. First, they must determine the metric form and the underlying machine learning models for fitting nuisance parameters (e.g., outcome function, propensity function, and plug-in learner). Second, they lack a specific focus on selecting a robust CATE estimator. To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection. The proposed DRM is nuisance-free, eliminating the need to fit models for nuisance parameters, and it effectively prioritizes the selection of a distributionally robust CATE estimator. The experimental results validate the effectiveness of the DRM method in selecting CATE estimators that are robust to the distribution shift incurred by covariate shift and hidden confounders.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.18392")
get_code_for_paper("2402.18392")
have("2402.18392")
Connect an agent — have() is free.