15

Jun

Teaser image to Conformal Meta-Learners for Predictive Inference of Individual Treatment Effects

Conformal Meta-Learners for Predictive Inference of Individual Treatment Effects

Ahmed M. Alaa, Berkley University of California

   15.06.2023

   6:00 pm - 7:30 pm

   LMU Institute of AI in Management via zoom

In the talk, ML and health databases enable personalized healthcare. Bayesian methods, using Gaussian processes, predict treatment effects. Model-free approaches, employing conformal prediction, offer guarantees. The discussion explores future research avenues.


Related

Link to tba

AI Keynote Series  •  08.08.2024  •  Online via Zoom

tba

Join the lecture with Michael Oberst from Johns Hopkins University.


Link to Representation Learning: A Causal Perspective

AI Keynote Series  •  18.07.2024  •  Online via Zoom

Representation Learning: A Causal Perspective

Lecture with Yixin Wang from University of Michigan, applying causal insights to create clear, efficient representations using counterfactuals.


Link to Interpretable prediction with missing values

AI Keynote Series  •  06.06.2024  •  Online via Zoom

Interpretable prediction with missing values

Missing values in healthcare data hinder interpretability and predictions. Fredrik Johansson's talk presents two solutions and suggests future research directions.


Link to Innovating AI Products for Social Good 
in the Age of Foundational Models

AI Keynote Series  •  23.05.2024  •  Online via Zoom

Innovating AI Products for Social Good in the Age of Foundational Models

Professor Qian Yang explores how LLMs necessitate considering societal impacts and innovating for social good in education and mental healthcare.


Link to Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification

AI Keynote Series  •  08.02.2024  •  LMU Institute of AI in Management via zoom

Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification

The presentation introduces causal scoring for decision-making, with interpretations.