# Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision

arXiv: https://arxiv.org/abs/2001.05699 · HTML: https://mlsanity.com/paper/2001.05699/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Optimal Best-Arm Identification in Bandits with Access to Offline Data](https://mlsanity.com/paper/2306.09048/) (2023)
- [Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support](https://mlsanity.com/paper/2107.11533/) (2021)
- [Contextual Online Pricing with (Biased) Offline Data](https://mlsanity.com/paper/2507.02762/) (2025)
- [Active and Passive Causal Inference Learning](https://mlsanity.com/paper/2308.09248/) (2023)

## Abstract (arXiv)

A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users' experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users' experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of "regret". We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.