Foundations of causal inference and open source causal analysis tools

Many key data science tasks are about decision-making. They require understanding the causes of an event and how to take action to improve future outcomes. Machine learning (ML) models rely on correlational patterns to predict the answer to a question but often fail at these decision-making tasks, as the very decisions and actions they drive change the patterns they rely on. Causal inference methods, in contrast, are designed to rely on patterns generated by stable and robust causal mechanisms, even as decisions and actions change. With insights gained from causal methods, the new, growing field of causal machine learning promises to address fundamental ML challenges in generalizability, interpretability, bias, and privacy.

In this talk, you will learn about the fundamentals of causal inference, including how a target question of cause and effect can be captured in a formal graphical model and answered systematically using available data. We will introduce a four-step causal modeling framework for analyzing decision-making tasks and walk-through code examples using the DoWhy, EconML libraries and ShowWhy no-code tools.