Asymptotically Best Causal Effect Identification with Multi-Armed Bandits
Authors: Alan Malek, Silvia Chiappa
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method by providing upper bounds on the sample complexity and an empirical study on artificially generated data. |
| Researcher Affiliation | Industry | Alan Malek Deep Mind London alanmalek@deepmind.com Silvia Chiappa Deep Mind London csilvia@deepmind.com |
| Pseudocode | Yes | Algorithm 1 CS-LUCB; Algorithm 2 CS-SE |
| Open Source Code | Yes | The code implementing the experiments is available at github.com/deepmind/abcei_mab. |
| Open Datasets | No | The paper uses 'artificially generated data' and describes the data generation process but does not provide concrete access information to a pre-existing public or open dataset. |
| Dataset Splits | No | The paper mentions data folds (Dη, Dτ, Dσ) used internally by the estimator but does not specify overall training, validation, and test dataset splits with percentages or sample counts for the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'ridge regression', 'logistic regression', and 'gradient-boosted regression trees' but does not specify their version numbers. |
| Experiment Setup | Yes | For the AIPW estimator, we used ridge regression and logistic regression to fit µx(Z) := Ep[Y |X = x, Z] and ex(Z) := p(X|Z) respectively. ... confidence intervals for logistic regression were approximated by a standard central limit theorem (CLT) confidence interval, h ˆσ2 + zαn/2 std(ˆσ2) n , ˆσ2 + z1 αn/2 std(ˆσ2) n i , with δ = 0.1 and αn = 6δ/πn2, following the construction described in Section 3.5. |