Join us for the IBM Research workshop on Causal Inference for Business Decision Making), during the IBM AI Research Week in Cambridge, Massachusetts!
Causal Inference is attracting a lot of attention in recent years due to its potential in addressing many challenges in Machine Learning and AI we face today. Interpretation of ML models, Fairness of ML processes/models and decision making based on data going beyond mere prediction involves causal inference principles. Existing ML models are good at prediction. However, to enable many other AI goals like interpretability/ Fairness/ decision making etc, algorithms must be equipped with counterfactual reasoning - answer to the "what if" questions. Causal understanding is quite important for answering these questions. There are a number of research areas within Machine Learning that use causal inference principles and they are becoming increasingly important.
We will bring speakers from academia, researchers in industry for a workshop focused on two areas in causal inference: a) Causal Inference for Healthcare and b) Learning causal dynamics of a system from trace/time series generated by it. This workshop will also have a poster session highlighting the work done at IBM in this space and also contributed posters from universities in the Boston Area.
Agenda in a nutshell (subject to change):
- 1:30pm: Welcome and Introduction
- 1:50pm: Theme A - Causality for Healthcare, with Miguel Hernan and Fredrik Johansson
- 3:30 - 4:15 pm - Poster/Networking Session with Snacks
- 4:20pm: Theme B - Learning Causal Dynamics from data, with David Jensenand Olga Vitek
Chair
Karthikeyan Shanmugam
To Register, please click on the Register button (above).
