Assessments

Overview

Assessment 1: Lab Diaries (10%)

Nine weekly diaries, one per lab (weeks 1–6 and 8–10). There are no labs in week 7 (test 1), week 11 (test 2), or week 12 (presentations). Your best eight diaries count (8 × 1.25%), so you may miss one without penalty. Each diary is graded satisfactory/not satisfactory. You receive full credit for submitting a satisfactory entry. Diaries are due by the end of the lab session.

What to write

Each diary is a short reflection (~150 words) covering:

1. What the lab covered and what you did.
2. A connection to the week's readings or lecture content.
3. One thing you found useful, surprising, or challenging.

Several labs have focussed exercises.

The labs are marked either full-credit/no-credit. If your reflection shows reflection and engagement you will get full credit (even if you do not get the exercises correct).

Format

Write each diary as a plain markdown (.md) file named by week number: lab-01.md, lab-02.md, …, lab-10.md (there is no lab-07.md). Use GitHub-flavoured markdown formatting: headings, paragraphs, bold, italics, and lists. Because you push diaries to GitHub, your files will render there automatically. These submissions build your markdown fluency; later in the course you will use Quarto to extend markdown to PDF and Word.

Submission

Push your diary files to your private GitHub Classroom repository set up in Lab 1. The commit timestamp is your submission record. Your repository is private and visible only to you and the course coordinator; no additional sharing step is needed.

Markdown example

Here is a minimal diary entry showing basic markdown formatting:

# Lab 01: Introduction to R

This week we installed R and RStudio, then ran our first script.
The exercise connected to the lecture on **causal questions** by
showing how we structure data for analysis.

I found the following steps useful:

- Creating an RStudio project
- Writing a short R script
- Pushing changes to GitHub

Assessment 2: In-Class Test 1 (20%) — 22 April

Covers material from weeks 1–6 (causal diagrams, confounding, ATE, effect modification). Test duration is 50 minutes. The allocated time is 1 hour 50 minutes. Required: pen/pencil. No devices permitted.

Test Location

The test is in class. Come to the seminar room (EA120) with a writing instrument.

Assessment 3: In-Class Test 2 (20%) — 20 May

Covers material from weeks 8–10 (heterogeneous treatment effects, machine learning, resource allocation, policy trees, and classical measurement theory). Same format and conditions as test 1.

Assessment 4: In-Class Presentation (10%) — 27 May

You will present your proposed project for the research report. You may present either (i) your Marsden EOI concept or (ii) your research report concept. Your job is to answer two questions for a non-specialist audience: what is it, and so what.

The presentation is 10 minutes, followed by 1 question. You must answer the question after your talk. You may ask one brief clarifying question before you answer.

You may use the whiteboard and paper notes. Do not use slides, handouts, devices, or other materials.

Your talk should cover the following points, in this order.

1. Title and motivation (what is it, so what).
2. Causal question, target population, exposure, and outcome.
3. A simple causal diagram showing your identification strategy.
4. Estimand and analysis plan (what you will estimate, and how).
5. One key limitation or risk, and how you will address it.

Assessment criteria are clarity and structure, causal reasoning, feasibility, and your response to the question.

Assessment 5: Research Report (40%) — Due 30 May

You choose your format

Students choose one of two formats for the research report:

• Option A: Research Report — quantify an average treatment effect using the NZAVS synthetic dataset.
• Option B: Marsden Fund EOI — write a first-round Marsden Fund Expression of Interest using the causal inference framework.

You must declare your choice by submitting the option form on Nuku by Friday 3 April (end of w6). If no declaration is received by this date, Option A is assumed.

Generate your data using the causalworkshop package:

# install (once)
install.packages("remotes")
remotes::install_github("go-bayes/causalworkshop@v0.2.1")

# generate data
library(causalworkshop)
d <- simulate_nzavs_data(n = 5000, seed = 2026)

Choose one exposure (community_group, religious_service, or volunteer_work) and one outcome (wellbeing, belonging, self_esteem, or life_satisfaction). Lab sessions support you in this assignment. We assume no statistical background.

Late Penalty

Late assignments, and assignments with extensions, may be subject to delays in marking and may not receive comprehensive feedback.

Assignments submitted late without an approved extension will incur a grade penalty of 5% of the total marks available for the assignment per day late (i.e., in 24-hr increments), up to a maximum of 5 days (up to 24 hrs late = −5%; up to 48 hrs late = −10%, etc.).

Assignments submitted more than five days late without an approved extension will not be graded unless exceptional circumstances are accepted by the Course Coordinator.

Option A: Research Report

Quantify the Average Treatment Effect of a specific exposure on a specific outcome using the synthetic NZAVS panel dataset generated by causalworkshop::simulate_nzavs_data(). You choose one of three exposures and one of four outcomes (see the data generation instructions above).

• Introduction: 800-word hard limit.
• Conclusion: 800-word hard limit.
• Methods/Results: concise, no specific word limit.
• APA style. Submit as a single PDF with R code appendix.

Assessment Criteria (Option A)

Stating the problem. State your question clearly. Explain its scientific importance. Frame it as a causal question. Identify any subgroup analysis (e.g. cultural group). Explain the causal inference framework for non-specialists. Describe ethics/policy relevance. Confirm data are from the NZAVS simulated dataset using three waves.

Determining the outcome. Define outcome variable $Y$. Assess multiple outcomes if applicable. Explain how outcomes relate to the question. Address outcome type (binary and rare?) and timing (after exposure).

Determining the exposure. Define exposure variable $A$. Explain relevance, positivity, consistency, and exchangeability. Specify exposure type (binary or continuous) and whether you contrast static interventions or modified treatment policies. Confirm exposure precedes outcome.

Accounting for confounders. Define baseline confounders $L$. Justify how they could affect both $A$ and $Y$. Confirm they are measured before exposure. Include baseline measures of exposure and outcome in confounder set. Assess sufficiency; explain sensitivity analysis (E-values) if needed. Address confounder type (z-scores for continuous; one-hot encoding for categorical with three or more levels).

Drawing a causal diagram. Include both measured and unmeasured confounders. Describe potential measurement error biases. Add time indicators ensuring correct temporal order.

Identifying the estimand. State your causal contrast clearly.

Source and target populations. Explain how your sample relates to your target populations.

Eligibility criteria. State the eligibility criteria for the study.

Sample characteristics. Provide descriptive statistics for baseline demographics. Describe magnitude of exposure change from baseline. Include references for more information about the sample. Make clear the data are simulated.

Missing data. Check for missing data. Describe how you will address the problem (IPCW).

Model approach. Decide between G-computation, IPTW, or doubly-robust estimation. Specify model. Explain how machine learning works. Address outcome specifics (logistic regression for rare binary outcomes; z-scores for continuous). Describe sensitivity analysis (E-values).

Option B: Marsden Fund Expression of Interest (EOI)

Write a first-round Marsden Fund Expression of Interest (EOI) following the RSNZ 2026 guidelines. Your research question must use the causal inference framework taught in this course. Assume an Ecology, Human Behaviour, and Evolution (EHB) panel.

Templates and Guidelines

Download the official 2026 RSNZ templates before you begin:

• EOI form (docx)
• Roles and Resources form (docx)
• Marsden Fund 2026 EOI Guidelines (pdf)

Formatting: 12-point Times New Roman, single spacing, 2 cm margins. Submit as a single PDF.

Required Sections

Section numbers follow the 2026 RSNZ EOI form.

1a. Research Title (max 25 words). Plain language, no jargon. The title should be accessible to a scientifically literate non-specialist.

1d. Research Summary (max 200 words). This summary must be standalone: assessors outside your discipline will read it. Answer four questions in this order:

1. What is the current state of the field? (1–2 sentences establishing the gap or problem.)
2. What do you aim to do? (State the causal question plainly.)
3. How will you do it? (Name the data source, design, and analytic approach.)
4. What do you expect to find? (One sentence on anticipated results and their significance.)

2. Vision Mātauranga (max 200 words). Describe how the proposed research relates to the four Vision Mātauranga (VM) themes: (i) indigenous innovation, drawing on Māori knowledge, resources, and people; (ii) taiao, achieving environmental sustainability through iwi and hapū relationships with land and sea; (iii) hauora/oranga, improving health and social wellbeing; (iv) mātauranga, exploring indigenous knowledge and its contribution to NZ research. If none of the themes apply, you may state "not applicable" with a considered justification.

3a. Abstract (max 1 page). Cover the following: aims of the research; importance of the research area; novelty, originality, insight, and ambition of the proposed work; potential impact; methodology; and your capacity to deliver.

3b. Benefit Statement (max 400 words, 1 page). Describe the economic, environmental, or health benefit of the research to New Zealand. Explain why NZ is the right place for this research and describe potential impacts for Māori. In a student context the benefit case may be aspirational, but it must be concrete.

3c. References (max 3 pages). Bold your own name. Include article titles and full author lists (up to 12 authors; use "et al." thereafter).

3d. Roles and Resources (max 1 page). Describe the contributions of each team member, the resources required, and any ethical considerations. Use the Roles and Resources form.

Assessment Criteria (Option B)

Research. Quantifiable impact potential through novelty, originality, insight, and ambition. Rigorous methods grounded in prior research. Ability and capacity to deliver.

Benefit. Economic, environmental, or health benefit to New Zealand. Rationale for NZ-based research. In a student context the benefit case may be aspirational but must be concrete.

Vision Mātauranga. Relation to VM themes; where relevant, engagement with Māori. "Not applicable" is acceptable with considered justification.

Causal reasoning (course-specific). Well-defined causal question, clearly stated causal estimand, appropriate identification strategy. This criterion carries substantial weight.

For the full Marsden Fund assessment criteria, see the RSNZ 2026 EOI Guidelines (pdf).

AI use in this course

Students may use AI tools in this course. AI use is permitted, but not required.

AI use policy

• You may use AI for coding help, brainstorming, and editing for clarity.
• You are responsible for all submitted work. Verify all claims, code, and references.
• You must be able to explain your work in your own words.
• For lab diaries and the final report, add a short note if AI use is substantial (tool, date, and how it was used).
• If AI output materially shaped your submission, acknowledge it as a source.
• AI tools are not permitted in in-class tests.
• Do not upload confidential, identifiable, or sensitive information