About – The Causal Layer

I’m an economist and data scientist working at the intersection of causal inference, pricing, and forecasting.

My day-to-day work is about turning a business question into an identification strategy, a model, and a defensible number. I care less about which method is fashionable this year and more about which one actually answers the question in front of me, and about being honest when the data can’t.

I’m currently a Senior Consultant at EY-Parthenon in Lisbon, working on econometric evaluation and decision analytics for public and private stakeholders. I hold a Ph.D. in Economics from ISEG – University of Lisbon, awarded with Distinction cum laude, on nonlinear panel-data estimators with heterogeneous effects. Before EY I spent three years as a Visiting Assistant Professor at ISEG, teaching applied econometrics, microeconomics and investment evaluation.

This blog is where I think out loud about the methods I use. Some posts are theory and intuition. Others are full walkthroughs with R code you can run. Most sit in between. The goal is rigour without textbook padding.

What I write about

Causal inference: DiD, RDD, IV, synthetic control, DML, causal forests. Identification strategies, DAGs, potential outcomes, and the robustness checks that keep you honest.
Pricing & demand: price elasticity, demand modelling, two-sided market pricing, dynamic pricing, MMM, causal price experiments.
Experimentation: A/B testing under interference, switchback experiments, CUPED, uplift modelling, heterogeneous treatment effects.
Forecasting: probabilistic forecasting, model evaluation, uncertainty quantification, and where forecasting stops being causal inference.

Background

Ph.D. in Economics: ISEG, University of Lisbon (2020 – 2026, Distinction cum laude)
MSc in Applied Econometrics and Forecasting: ISEG, University of Lisbon
BSc in Management: Faculty of Economics, University of Coimbra
Senior Consultant: EY-Parthenon (2024 – present)
Visiting Assistant Professor: ISEG, University of Lisbon (2021 – 2024)

Affiliations

Member, REM: Research in Economics and Mathematics
Member, CEMAPRE: Center for Applied Mathematics for Forecasting and Economic Decision

All views are my own. Code for every post is available on https://github.com/joaovieira22.

--- title: "About" toc: false about: template: trestles image: foto3.jpeg links: - icon: linkedin text: LinkedIn href: https://www.linkedin.com/in/joao-vieira-iseg/ - icon: envelope text: Email href: mailto:joaoviei7a@gmail.com - icon: github text: GitHub href: https://github.com/joaovieira22 --- I'm an economist and data scientist working at the intersection of **causal inference**, **pricing**, and **forecasting**. My day-to-day work is about turning a business question into an identification strategy, a model, and a defensible number. I care less about which method is fashionable this year and more about which one actually answers the question in front of me, and about being honest when the data can't. I'm currently a Senior Consultant at **EY-Parthenon** in Lisbon, working on econometric evaluation and decision analytics for public and private stakeholders. I hold a **Ph.D. in Economics** from **ISEG – University of Lisbon**, awarded with Distinction *cum laude*, on nonlinear panel-data estimators with heterogeneous effects. Before EY I spent three years as a Visiting Assistant Professor at ISEG, teaching applied econometrics, microeconomics and investment evaluation. This blog is where I think out loud about the methods I use. Some posts are theory and intuition. Others are full walkthroughs with R code you can run. Most sit in between. The goal is rigour without textbook padding. ## What I write about - **Causal inference**: DiD, RDD, IV, synthetic control, DML, causal forests. Identification strategies, DAGs, potential outcomes, and the robustness checks that keep you honest. - **Pricing & demand**: price elasticity, demand modelling, two-sided market pricing, dynamic pricing, MMM, causal price experiments. - **Experimentation**: A/B testing under interference, switchback experiments, CUPED, uplift modelling, heterogeneous treatment effects. - **Forecasting**: probabilistic forecasting, model evaluation, uncertainty quantification, and where forecasting stops being causal inference. ## Background - **Ph.D. in Economics**: ISEG, University of Lisbon *(2020 – 2026, Distinction cum laude)* - **MSc in Applied Econometrics and Forecasting**: ISEG, University of Lisbon - **BSc in Management**: Faculty of Economics, University of Coimbra - **Senior Consultant**: EY-Parthenon *(2024 – present)* - **Visiting Assistant Professor**: ISEG, University of Lisbon *(2021 – 2024)* ## Affiliations - Member, **REM**: Research in Economics and Mathematics - Member, **CEMAPRE**: Center for Applied Mathematics for Forecasting and Economic Decision *All views are my own. Code for every post is available on https://github.com/joaovieira22.*