Most ML models excel at prediction, answering questions like "Who will buy our product?" or "Which customers are likely to churn?". But when it comes to making actionable decisions, prediction alone can be misleading. Correlation does not imply causation, and business decisions require understanding causal relationships to drive the right outcomes.

In this talk, we will explore how causal machine learning, specifically uplift modeling, can bridge the gap between prediction and decision making. Using a real-world use case, we will showcase how uplift modeling helps identify who will respond positively to interventions while avoiding those who they might deter.

As machine learning models become more accurate and complex, explainability remains essential. Explainability helps not just with trust and transparency but also with generating actionable insights and guiding decision-making. One way of interpreting the model outputs is using SHapley Additive exPlanations (SHAP). In this talk, I will go through the concept of Shapley values and its mathematical intuition and then walk through a few real-world examples for different ML models. Attendees will gain a practical understanding of SHAP's strengths and limitations and how to use it to explain model predictions in their projects effectively.

Collaborating on code and software is essential to open science—but it’s not always easy. Join this BoF for an interactive discussion on the real-world challenges of open source collaboration. We’ll explore common hurdles like Python packaging, contributing to existing codebases, and emerging issues around LLM-assisted development and AI-generated software contributions.

We’ll kick off with a brief overview of pyOpenSci—an inclusive community of Pythonistas, from novices to experts—working to make it easier to create, find, share, and contribute to reusable code. We’ll then facilitate small-group discussions and use an interactive Mentimeter survey to help you share your experiences and ideas.

Your feedback will directly shape pyOpenSci’s priorities for the coming year, as we build new programs and resources to support your work in the Python scientific ecosystem. Whether you’re just starting out or a seasoned developer, you’ll leave with clear ways to get involved and make an impact on the broader Python ecosystem in service of advancing scientific discovery.