This notebook introduces the code framework for reproducing the results of the NeurIPS paper, Alan Malek, and Silvia Chiappa. "Asymptotically Best Causal Effect Identification with Multi-Armed Bandits." Advances in Neural Information Processing Systems 34 (2021). The project name is an abbreviation of the title.

Roughly, we have a causal effect and several estimators that can measure it. We will try to select the estimator with the best cost-adjusted asymptotic variance in a sequential decision making problem where each round, we choose an estimator and obtain a sample from the covariates it requires. We use a best-arm-identification algorithm to choose which estimator to sample from.

This project contains code to:

1. describe and simulate data from a graphical model
2. Fit the causal effects with nuisance functions given this data
3. Construct confidence intervals for this causal effect,
4. Run a bandit algorithm using these confidence intervals
5. Provide an example notebook that generates the plots in the paper.

The companion colab notebook thoroughly describes the intended usesage.

@article{malek2021asymptotically,
  title={Asymptotically Best Causal Effect Identification with Multi-Armed Bandits},
  author={Malek, Alan and Chiappa, Silvia},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

This is not an official Google product.