This workshop introduces a comprehensive framework for causal analysis, enabling participants to understand and model cause-effect relationships across domains, such as medicine (e.g., clinical trials), retail (e.g. customer engagement), economics (e.g., policy evaluation), and technology (e.g., recommendation systems).

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We begin by examining the limitations of predictive models and correlations, highlighting why they are often insufficient for answering “what if” questions or reasoning about interventions. To overcome these challenges and avoid common pitfalls, we present the foundations of causal reasoning, including counterfactuals, interventions, and causal graphs, and demonstrate how these concepts apply to real-world problems.

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The workshop is structured around two core areas: causal discovery and causal inference. Causal discovery focuses on uncovering underlying causal structures directly from data. We will explore a range of methods, with particular attention to their assumptions and limitations. Causal inference, in contrast, aims to estimate the effect of interventions or treatments on outcomes. We will cover both quasi-experimental approaches and modern machine learning techniques, along with practical strategies for evaluating their validity.

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Finally, two extended hands-on sessions will allow participants to apply these theoretical concepts in practice and quickly get started with causal machine learning using Python.