Name: Introduction to Targeted Maximum Likelihood Estimation from scratch
Start: 2025-06-04T17:30:00-04:00
End: 2025-06-04T18:30:00-04:00

Targeted Maximum Likelihood Estimation (TMLE) is a powerful, flexible framework for drawing causal conclusions from observational data. Unlike traditional statistical methods, TMLE is designed to explicitly target the causal parameter of interest, combining machine learning with rigorous statistical theory to reduce bias and improve efficiency. In this session, we’ll build TMLE from the ground up and explore how it enables us to emulate randomized controlled trials using real-world data, even in the presence of confounding and missingness.

Donald Szlosek

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Donald Szlosek is a seasoned research biostatistician with over a decade of experience in clinical trials, diagnostic medicine, and real-world evidence studies. He specializes in advanced statistical methods, including targeted maximum likelihood estimation (TMLE) and the validation of machine learning algorithms in healthcare. Donald is passionate about bridging methodological rigor with practical impact to improve patient outcomes and data-driven decision-making.

Introduction to Targeted Maximum Likelihood Estimation from scratch

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Introduction to Targeted Maximum Likelihood Estimation from scratch

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