# Machine Learning Prescriptive Canvas for Optimizing Business Outcomes

arXiv: https://arxiv.org/abs/2206.10333 · HTML: https://mlsanity.com/paper/2206.10333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [A Survey on Practical Applications of Multi-Armed and Contextual Bandits](https://mlsanity.com/paper/1904.10040/) (2019)
- [A Survey of Learning Causality with Data: Problems and Methods](https://mlsanity.com/paper/1809.09337/) (2018)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints](https://mlsanity.com/paper/2008.06293/) (2020)
- [Contextual Multi-Armed Bandits for Causal Marketing](https://mlsanity.com/paper/1810.01859/) (2018)
- [Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset](https://mlsanity.com/paper/1711.00083/) (2017)
- [Ten Research Challenge Areas in Data Science](https://mlsanity.com/paper/2002.05658/) (2020)
- [The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About](https://mlsanity.com/paper/2002.07069/) (2020)

## Abstract (arXiv)

Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.