Jamilla Cooiman, owner of Causal Academy, provides in-company training for data teams that want to build stronger practical skills in causal inference.
The trainings are designed for data professionals who want to develop or deepen their skills in causal inference. This includes data scientists, data analysts, product analysts, and related analytical roles.
The training is especially relevant for teams working on questions related to impact evaluation, targeting and personalization, pricing, marketing effectiveness, or other decision problems where causal reasoning matters.
Participants are expected to have experience working with data and basic statistical concepts. No prior knowledge of causal inference is required. When teams already have experience in the field, the level and focus of the training can be adjusted accordingly.
The content of each training can be adapted to organizational needs. Topics that can be covered include:
Fundamentals of causal inference.
Why causal inference matters for decision support, how it differs from predictive modelling, the meaning of causal effect quantities such as ATT, ATE, and CATE, and the core assumptions required for causal analysis.
Preparation stages of causal analysis workflows.
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.
From linear regression models to more advanced causal machine learning methods, applied to both average treatment effects and heterogeneous effects (CATE / uplift modelling).
Diagnostics and robustness analysis.
Diagnostic tools for assessing core causal assumptions and strategies to address violations, including positivity diagnostics and sensitivity analyses for unobserved confounding.
Below are examples of training tracks that can be delivered depending on the team’s background, goals, and use cases.
A foundational training track focused on building a clear understanding of what causal inference is, why it differs from standard predictive modelling, and what a practical causal workflow looks like in observational settings.
Topics can include:
why causal inference is needed for decision support
causal targets and their differences
what is required to estimate causal effects from observational data
unconfoundedness and the role of variable adjustment
the use of causal graphs in observational causal analyses
the use of target trial emulation to define well-defined causal projects
why models are needed for causal analysis and how effects can be estimated using linear regression
sensitivity analysis for unobserved confounding
This track is well suited for teams that want to build a strong shared foundation in causal inference and understand the main steps involved in applied causal projects.
A more advanced training track focused on targeting and personalization problems, where the goal is not only to understand whether an intervention or decision works on average, but for whom it works best.
Topics can include:
why targeting and personalization are causal problems
heterogeneous treatment effects and the CATE
how causal machine learning methods can be used to quantify effect heterogeneity
how these estimates can support more effective decision-making and resource allocation
This track is well suited for teams that already have some familiarity with causal inference and want to move toward more advanced decision support in targeting and personalization contexts.
Trainings can be delivered in a range of formats depending on the team’s goals, background, and availability.
A common format is a series of half-day sessions spread over multiple weeks. This allows participants to build skills progressively and practice with concepts between sessions. Other formats, such as single-session workshops or more intensive full-day trainings, can also be arranged depending on the needs of the team.
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 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 and include examples, case studies, coding demonstrations, and exercises throughout. Participants retain access to all training materials after completion of the training(s).
Training programmes can be delivered in standardized formats or tailored to organizational needs. Customization can include a focus on specific methodological areas, alignment with internal business use cases, or adjustments in depth depending on the team’s background.
If you would like to learn more or schedule a training, please reach out via email: