CARMA 2026 is pleased to announce the four tutorials that will be offered as part of the 8th International Conference on Advanced Research Methods and Analytics, which will take place in Valencia, Spain, from 1 to 3 July 2026. The conference focuses on AI, Internet data and computational methods in economics and the social sciences.
The tutorial program brings together practical and methodological sessions for researchers, doctoral students, data scientists and practitioners interested in advanced research methods, AI-driven analysis and data-intensive approaches.
Tutorial 1: Getting an h-index of 100 in 20 years or less!
This tutorial, presented by Prof. Eamonn Keogh, discusses the h-index, its limitations, and the factors that influence research visibility and citation impact. The session will address topics such as the Matthew Effect, making research outputs more reusable and findable, choosing publication venues, open access, networking and responsible strategies for increasing academic impact. The tutorial is especially relevant for doctoral students, early-career researchers and senior academics who mentor junior colleagues.
More information here.
Tutorial 2: Interactive Workshop on Applied Time Series Forecasting
This four-hour workshop, led by Dr. Dominik Heinisch and Benedikt Lugauer from Lidl Data & AI, offers an applied introduction to time series forecasting in energy analytics. The session will cover classical forecasting methods, robust baselines, evaluation schemes, real-world data challenges and recent time series foundation models. It will also include a hands-on coding component focused on hindcasting, counterfactual analysis and predictive forecasting.
More information here.
Tutorial 3: Using LLMs for Constructing Quantitative Indicators from Text
This tutorial, presented by Xavier Martínez-Barbero, introduces a reproducible framework for building quantitative indicators from textual data using Large Language Models. Participants will learn how to structure a measurement pipeline that includes text preprocessing, segmentation, rubric-based prompt design, LLM-based scoring, reliability and validity assessment, human expert benchmarking and calibration. The session is designed for researchers interested in text-based measurement and AI-supported empirical research.
More information here.
Tutorial 4: Causal Random Forests: Estimating Heterogeneous Treatment Effects with Machine Learning
This tutorial, presented by Eugeni Gil-Ocaña and Prof. Ana García-Bernabeu, focuses on causal machine learning and the estimation of heterogeneous treatment effects. It will introduce the potential outcomes framework, identification assumptions, Conditional Average Treatment Effects, Robinson decomposition, honest sample splitting and the Generalized Random Forests framework. The tutorial will also include a hands-on session using Python and the EconML package.
More information here.
A broad and practical tutorial program
Together, the four tutorials reflect the methodological scope of CARMA 2026: research assessment and academic impact, forecasting and business analytics, LLM-based measurement, and causal inference with machine learning. They provide an opportunity for participants to strengthen their methodological skills and engage with applied tools that are increasingly relevant across economics, business, public policy and the social sciences.