MTMCW: Causality and Decision-Making

Module code: MTMCW

Module provider: Meteorology; School of Mathematical, Physical and Computational Sciences

Credits: 20

ECTS credits: 10

Level: 7

When you’ll be taught: Semester 2

Module convenor: Professor Rosalind Cornforth, email: r.j.cornforth@reading.ac.uk

Module co-convenor: Professor Ted Shepherd, email: theodore.shepherd@reading.ac.uk

Additional teaching staff 1: Dr Eviatar Bach, email: e.bach@reading.ac.uk

Additional teaching staff 2: Dr Nachiketa Chakraborty, email: n.chakraborty@reading.ac.uk

Pre-requisite module(s): Students taking this module must have completed MTMDCS, or have a basic background in probability and statistics. (Open)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2026/7

Available to visiting students: Yes

Talis reading list: No

Last updated: 26 March 2026

Overview

Module aims and purpose

Climate-related decisions are made at the intersection of science, policy, and society, shaped by uncertainty, competing values, and unequal power. This module trains students to navigate that complexity by combining formal causal inference methods with critical understanding of how climate problems are defined, whose knowledge counts, and who wins when resources are limited.

Students will learn to:

• Build and apply causal networks to model climate risks and evaluate interventions.
• Understand how institutional position, epistemic plurality, and power shape what gets modelled and optimised.
• Critically assess the role of AI and quantitative tools in climate decision-making
• Recognise that technical choices (what variables to include, what relationships to assume, what outcomes to prioritise) are also political and ethical choices.

The module begins with political ecology and climate justice, examining how problems are framed, whose claims are heard, and how power operates in climate governance. It then moves into statistical foundations and causal inference methods (causal networks, discovery, estimation), always returning to the question: whose world are we modelling, and who benefits?

This approach prepares students to work responsibly as climate scientists, analysts, and advisors, able to use powerful technical tools while understanding the politics that surround them.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Critically analyse how climate problems are defined, whose knowledge is privileged, and how power shapes decision-making in adaptation and risk assessment contexts (political ecology, climate justice).
2. Describe and apply the main modelling tools of causal inference, including causal networks, causal discovery, and estimation of causal effects.
3. Build causal models that are transparent about assumptions, recognising that choices about variables, relationships, and interventions reflect values and institutional priorities.
4. Evaluate the role of AI and machine learning in climate decision-making, identifying what assumptions are embedded, whose outcomes are optimised, and who is made visible or invisible by algorithmic systems.
5. Draw correct inferences about climate risks and impacts across populations with differentiated vulnerability, using both qualitative and quantitative evidence.
6. Recognise the role of political ecology, governance, and justice in shaping national and international climate policies and institutions (including IPCC negotiations, adaptation finance, and climate litigation.

Module content

The module is structured in three phases, with a unifying thread: Before you build a model, someone has already decided what the problem is, whose outcomes matter, and what counts as success.

Phase 1: Foundations – Problem Definition, Power, and Justice (Weeks 1to 3)

• Uncertainty and epistemic humility: what do we know, and what don’t we know?
• Political ecology: how institutional position shapes problem definition and causal reasoning
• Climate justice and ethics: who owes what to whom? Justice claims, power asymmetries, and negotiated outcomes
• Dynamic case studies from the UK and internationally, COP negotiations, climate litigation

Phase 2: Statistical and Probabilistic Foundations (Weeks 4 to 6)

• Probability and Bayesian reasoning
• Correlation, regression, and causal thinking
• Why “controlling for X” is a causal claim, not just a statistical one

Phase 3: Causal Inference and Decision-Making (Weeks 7 to 12)

• Causal networks (Directed Acyclic Graphs): building, interpreting, and estimating causal effects
• The role of expert knowledge and assumptions in network construction
• Causal discovery: using machine learning to learn causal structure from data
• AI and climate decision-making: opportunities, risks, and embedded values
• Decision-making under uncertainty: storylines, scenarios, and adaptation pathways

Cross-cutting themes:

• Power dynamics and whose knowledge counts
• Equity and inclusiveness in climate governance
• The role of AI in amplifying or marginalising voices
• International environmental law, UNFCCC, and institutional responses
• Climate risk assessments and “decision-relevant” science

Structure

Teaching and learning methods

The module uses active, interdisciplinary pedagogy designed to develop both technical skills and critical reasoning:

• Interactive lectures where students construct knowledge through structured activities (not passive transmission)
• Computer-based practicals using Python and causal inference software (e.g., DAGitty, causal discovery algorithms)
• Case study-based seminars examining real-world climate decisions through political ecology and justice frameworks
• Group work and role-play to experience how power, mandate, and institutional position shape problem definition and resource allocation
• Invited speakers and guest panels from climate science, policy, law, and civil society
• Guided independent study with scaffolded readings and reflection prompts

Practical sessions are designed to illustrate concepts from lectures and give students hands-on experience implementing methods through real-world case studies.

Study hours

At least 60 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): 10% of the total marks available for that piece of work will be deducted from the mark for each calendar day (or part thereof) following the deadline up to a total of three calendar days;
• where the piece of work is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in you Individual Learning Plan), the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan), no penalty shall be imposed;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three calendar days of the deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): no penalty will be applied;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a grade of Fail will be awarded.

Where a piece of work is submitted late after a deadline which has been revised owing to an extension granted through the Assessment Adjustments policy and process (self-certified or otherwise), it will be subject to the maximum penalty (i.e., considered to be more than three calendar days late). This will also apply when such an extension is used in conjunction with a DAS-agreed extension as a reasonable adjustment.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

• Short interactive exercises and quizzes in lectures
• Informal feedback in practical sessions
• Peer discussion and group reflection activities

Reassessment

Additional costs