Causal Reasoning and Machine Learning

Learning Outcomes

* ML's limitations: review of relations between questions asked in machine learning and causality * Philosophical titbits: Asymmetry of cause and effect, co-ordination of effects due to hidden causes. * Machinery of probabilistic graphical models - Graphical Markov models; conditional independence and d-separation _ Structural equation modelling - Interventions and do-calculus - Simpson's paradox and confounders - Front-door and back-door criteria for identifying causal effects from observable data * Cause-effect , covariant shifts: If A and B are correlated, what is the direction of the arrow linking A and B? Independence of causal mechanism from input. Covariant shifts and regression modelling. * Representation learning and causality: Disentangling of representations via causal mechanisms and invariant risk minimisation. * Counterfactuals: The ability to answer ``what-if" questions requires a causal mechanism not mere correlations. Application example: eliminating spurious correlations in classification problems. * Potential outcomes, A/B testing and randomised trials: Explaining the relations between different approaches to and techniques in causal analysis. Applications to healthcare. * Fairness and bias: Fairness of algorithms from a process (disparate treatment) or an outcome perspective (disparate outcome). Fairness and bias from a causal lens and a counterfactual perspective.

Lectures, lab exercises, student-led presentations on specific topics