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.

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 the lab diary GitHub repository created in Lab 1. The commit timestamp is your submission record.

# 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.

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

10-minute presentation summarising the student's study. Assessment criteria: clarity, efficiency, and quality of presentation.

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

# 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)

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: 1,500-word limit.
• Conclusion: 1,500-word 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.

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). State the current state of the field, the aims of your research, the methods you will use, and the expected outcome. This summary must be standalone: assessors outside your discipline will read it.

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).