Technical training in causal inference for data professionals.
Jamilla Cooiman, founder of Causal Academy, provides in-company training focused on building practical causal inference skills within data teams.
The trainings are designed for data professionals seeking to develop or deepen their skills in causal inference. This includes data scientists, data analysts, product analysts, and related analytical roles.
Participants are expected to have some prior experience working with data and basic statistical concepts. No prior knowledge of causal inference is required. When participants already have experience in the field, the level and focus of the training can be adjusted accordingly.
Trainings are most commonly delivered as half-day sessions spread over multiple weeks. This format allows participants to build skills progressively and practice with concepts between sessions. In addition, full-day workshops can be organized when a more intensive format is preferred.
Trainings can be delivered online to international teams or on-site within the Netherlands. All sessions are conducted in English (or Dutch if preferred).
The content of each training can be adapted to organizational needs. Topics that can be covered include:
Fundamentals of causal inference. This includes why causal inference is required for decision support, how it differs from predictive modelling, the meaning and relevance of causal effect quantities such as ATT, ATE, and CATE, and the core assumptions required for causal analysis.
Preparation stages of causal analysis workflows. This includes translating business questions into well-defined causal projects, constructing causal graphs for observational analyses, and mapping these graphs to adjustment strategies under realistic data constraints.
Estimation approaches for causal inference. This ranges from linear regression models to more advanced causal machine learning methods, applied both to general average treatment effects and heterogeneous effects (CATE/uplift modelling).
Diagnostics and robustness analysis. This includes diagnostic tools for assessing core causal assumptions and strategies to address violations, such as positivity diagnostics and sensitivity analyses for unobserved confounding.
The trainings are delivered with theory as the foundation and practice as the primary focus. Methods are discussed in terms of what is ideal from a theoretical perspective, what is feasible in applied environments, and what trade-offs arise in real projects. Sessions are interactive in structure and include examples, case studies, coding demonstrations, and exercises throughout. Participants retain access to all training materials after completion of the programme.
Training programmes can be delivered in standardized formats or tailored to organizational needs. Customization can include focusing on specific methodological areas, aligning content with internal business use cases, or adjusting depth based on participant background.
If you would like to learn more or schedule a training, please reach out via email: