CV
RESEARCH INTERESTS
Causal inference · Transportability & generalisation of trial findings · Doubly robust machine learning · Food systems modelling · Complex survey design · Econometric evaluation of health policies
RESEARCH STATEMENT
Constanza is a finishing PhD (c) in Social Statistics at the University of Manchester, specialising in causal inference methods for evaluating health and food policy. Her doctoral work develops novel doubly robust machine learning estimators — combining Bayesian additive regression trees and gradient boosting — to generalise randomised trial findings to national target populations in non-nested designs. This positions her at the intersection of causal modelling, food labelling policy, and population-level inference. Prior to her doctorate, she built and directed national data infrastructure at Chile’s National Institute of Statistics, providing rare expertise in large-scale food system datasets, multistage survey design, and algorithmic data quality control at census scale.
RESEARCH EXPERIENCE
University of Manchester | Manchester, United Kingdom PhD (c) | Oct 2021 – Present Thesis: Estimating population average treatment effects for food labelling policy: A causal inference approach for generalising trial findings to target populations in non-nested designs.
- Developed and validated a novel doubly robust ML estimator pipeline (BART + gradient boosting) to adjust for confounding and transport causal effects from a food labelling RCT to the UK adult population, combining trial data with NDNS observational data via augmented inverse probability weighting and G-computation.
- Designed and implemented an online randomised controlled trial (n = 498 representative UK sample via Prolific) to test the causal effects of food label granularity on food choice and working memory.
- Built a cost-effectiveness and microsimulation model to quantify the population-level economic impact of alternative food labelling policies, translating trial findings into policy-relevant outcomes (QALYs, dietary change).
- Conducted feature engineering to integrate large-scale health and nutritional datasets (NDNS, UK Biobank-adjacent) for use in causal modelling.
- Published 2 peer-reviewed papers (2025–2026) and 1 manuscript in preparation; presented findings at 5 national and international conferences.
Alan Turing Institute | London, United Kingdom Turing Enrichment Visitor Researcher | Jan – Jul 2025
- Developed a principled causal inference framework for enhancing the external validity of RCTs, fusing trial data with observational population datasets through statistical matching and reweighting.
- Co-organised and secured £2,000 funding for the institute-wide workshop ‘Digital Twins: A Collaborative Workshop for Practical Application and Knowledge Exchange’.
- Presented doctoral research at the ATI PhD Research Showcase and attended the Oxford Machine Learning Summer School (MLx Generative AI).
University of Manchester | Manchester, United Kingdom Research Assistant | Apr 2024 – Apr 2025
- Designed and executed a systematic literature review on the online illicit drug market; synthesised findings into policy-facing reports.
LEADERSHIP & DATA ANALYSIS EXPERIENCE
National Institute of Statistics (INE) | Santiago, Chile Chief, Department of Agricultural Statistics | Apr 2018 – Jan 2019
- Directed Chile’s national agricultural statistics programme, leading a multidisciplinary team of 15 with a $1M USD annual budget.
- Designed the sampling and digital data capture strategy for the 2021 Agricultural Census, deploying an offline-capable field platform across remote rural locations.
- Oversaw the production of monthly, quarterly, and annual statistical reports for 12 longitudinal surveys.
Coordinator, Department of Agricultural Statistics | Apr 2015 – Mar 2018
- Designed and coordinated 6 nationally representative longitudinal surveys using complex multistage sampling.
- Developed and deployed time-series predictive algorithms to flag data inconsistencies; reduced data lifecycle by one month.
- Authored the Chilean case study for the FAO World Programme for the Census of Agriculture 2020 (Volumes 1 & 2).
EDUCATION
University of Manchester | Oct 2021 – May 2026 (expected) PhD in Social Statistics
University of Manchester | Oct 2020 – Sept 2021 MSc Social Research Methods and Statistics (Distinction)
Universidad Santiago de Chile | Mar 2013 – Jul 2016 BSc in Economics (Distinction)
Universidad de Valparaíso | Mar 2003 – Dec 2008 BA in Sociology (Distinction)
PUBLICATIONS & PEER REVIEW
Journal Articles
- Avalos, C., Wang, Y., & Shryane, N. (in preparation). On the selection of covariates for transportability: A doubly robust machine learning approach for generalising a food labelling trial with mixed-type data.
- Avalos, C., Wang, Y., & Shryane, N. (2026). Food label readability and consumption frequency: Isolating content-specific effects via a non-equivalent dependent variable design. Nutrients, 18(2), 197.
- Avalos, C. (2025). Food label granularity and working memory: Effects on food choice in a randomised controlled trial. Journal of Health, Population and Nutrition, 44, 375.
Technical Reports
- FAO (2019). World Programme for the Census of Agriculture 2020, Vols 1 & 2. Chilean case study.
- INE Chile (2018). Statistical Yearbook 2018 — National Agricultural Statistics chapter.
- INE Chile (2018). Bulletin of Livestock, Crop, and Dairy Food Industry Statistics.
Peer Review
- Reviewer, British Food Journal.
TEACHING & MENTORING
University of Manchester | Graduate Teaching Assistant | 2023–2025
- SOST70032: Complex Survey Designs and Analysis
- SOST70172: Quantitative Evaluation of Policies, Interventions and Experiments
- SOST70520: Methodology and Research Design
- Helpdesk Support TA: one-on-one statistical modelling and R troubleshooting.
CONFERENCES & TALKS
PhD Research Showcase, Alan Turing Institute Jul 2025 · London SoST RAGS Seminar, University of Manchester Jul 2025 · Manchester Geneva Health Forum, University of Geneva May 2024 · Geneva Royal Statistical Society International Conference Sept 2023 · Harrogate Postgraduate Summer Research Showcase, University of Manchester Jun 2022 · Manchester
GRANTS & AWARDS
- Grassroots Training Fund, Alan Turing Institute — £2,000 (Jul 2025)
- Enrichment Student Funding, Alan Turing Institute — £1,000 (Jun 2025)
- ELLIS Summer School Scholarship — £500 (Jun 2024)
- Student Bursary, Manchester Jean Monnet Centre of Excellence — £800 (May 2024)
- Fieldwork Bursary, School of Social Sciences — £3,000 (Nov 2022)
- PhD Fellowship, ANID (Chilean National Research and Development Agency)
- Master’s Fellowship, ANID
SERVICE & OTHER
- PGR Student Representative, University of Manchester (2023–2024)
- Social Stats Hackathon 2023 Organiser
- Entrepreneurial Project: TRAILACADEMY.RUN
TECHNICAL SKILLS
- Methodologies: Causal Inference, Doubly Robust Machine Learning (BART, Gradient Boosting), Econometric Methods, Microsimulation, Complex Survey Design.
- Software: R (primary), Python (Keras/TensorFlow), Stata, LaTeX.