QMEG Masterclass: Introduction to Causal Inference with Machine Learning
Hosted by Southampton Education School || QMEG
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What to look forward to
This hands-on session will introduce Double Machine Learning for estimating valid causal effects, with a practical Python example. No prior causal-inference knowledge will be needed.
Inside the event
Many research questions are causal, yet standard machine learning models are built for prediction, not for estimating the effect of an intervention. Double Machine Learning (DML) bridges this gap, letting researchers use flexible ML models to control for complex confounding while still producing valid effect estimates and confidence intervals.
This online seminar introduces the core ideas behind DML, including orthogonalisation and cross-fitting, with a worked example in Python. We will also cover the key assumptions and limitations.
Suitable for researchers and postgraduate students with basic regression knowledge; no prior causal inference experience needed.
A seat from anywhere.
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Last checked: Oct 16, 2026. Confirm current availability before making plans.
Organizer page checked: Oct 16, 2026. Check registration for the latest details.