This project contains example notebooks exhibiting confidence sequences that are robust, i.e., converge for observations with infinite variance.
- Robust Mean Demo: The basic technique for covering the running conditional mean in a nonstationary environment. Includes the use of approximate sufficient statistics to bound space and time complexity.
- Off-Policy Quantile Demo: Off-policy quantile identification functions can exhibit infinite variance (unlike the on-policy case). In this case the importance weights are Pareto distributed with infinite variance: this might arise in a continuous action problem.
- Expectile Demo: Expectile identification functions can exhibit heavy tails. In this case the observation is Lognormal distributed.
- Anytime-valid off-policy inference for contextual bandits: contains important background material on anytime valid off policy inference. The running counterfactual mean is covered even with unbounded importance weights; but finite variance is assumed.
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A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean: extends the previous paper to support adapting to an unknown moment
$q \in (1, 2]$ . - Time-uniform confidence bands for the CDF under nonstationarity: extends the previous two papers to support covering the entire counterfactual CDF rather than just the mean.
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