JJ Valletta Memorial Lecture 2025 From causal inference to machine learning and back: a two-way street towards better science

This year's memorial lecture features Professor Karla Diaz Ordaz of University College London, who will present ‘From causal inference to machine learning and back: a two-way street towards better science'.

Machine learning methods have become established for prediction problems, but there is increasing interest in using these algorithms for causal inference. However, causal effect estimation often involves counterfactuals, and prediction tools from the machine learning literature cannot be used ‘out-of-the-box' for causal inference.

At the same time, there is an increasing interest in using causal reasoning when building and interpreting machine learning algorithms. Doing so can help reduce unfairness and other algorithmic biases stemming from the training data not being representative of the target population. Causality can also help with interpretability and explainability of machine learning outputs.

In this talk, Karla will review Causal Machine learning, a framework to ‘de-bias' standard machine learning algorithms so they perform well for causal tasks and will discuss the role causal inference can play in machine learning to improve fairness and explainability of so-called ‘black-box' models.

This two-way street opens the way to making better use of the data and obtaining reliable answers to real-life scientific problems, while maintaining good statistical principles.

The seminar, from 2pm to 3pm, will be followed by a social reception from 3pm to 4pm.