Causal Reasoning and Machine Learning

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Evaluate models and algorithms proposed in the research literature to identify explanatory mechanisms behind data patterns
• Ability to demonstrate how such models capture changes of probability upon conditioning, upon performing actions or upon posing what-if scenarios.
• Ability to construct and reason with deterministic and probabilistic models that represent hypothetical causal mechanisms

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Create models for simulating data with different explanatory mechanisms
• Systematically work with data and within state-of-the-art software environments to learn patterns or concepts

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Appreciate the difference between predictive ability and explanatory adequacy
• Distinguish between the roles of observational and experimental data
• Identify the necessity of causal reasoning in application domains

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Appreciate how working with patterns in data that have societal implications

* 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.

Teaching and learning methods

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

Resources & Reading list

Internet Resources

Causality for machine learning.

Textbooks

J. Pearl, M. Glymour, and N. P. Jewell (2016). Causal Inference in Statistics: A Primer. John Wiley & Sons.

Judea Pearl and Dana Mackenzie (2018). The Book of Why. New York: Basic Books.

J. Peters, D. Janzing, and B. Schoelkopf (2017). Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press.

Assessment strategy

Coursework only: assessment based on presentations and reports.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External