AI Research Week Workshop - Causal Inference for Business Decision Making     


AI Research Week Workshop - Causal Inference for Business Decision Making - overview


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 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 increasingly becoming 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. 

Chairs: Karthikeyan Shanmugam (IBM Research, NY), Kristjan Greenewald (IBM Research, Cambridge MA), Chen Yanover (IBM Research - Haifa).

Venue: DR 4 MIT Samberg Center, Cambridge, MA

Date/Time: October 3, 2018. 1:00-6:00 pm.

Registration Link:

AI Research Week: This workshop is part of a wider AI Research Week organized by IBM Research Cambridge coinciding with the 1-year anniversary of MIT-IBM Watson AI Lab. For information about the AI Research week, please visit:



We welcome student and IBM posters. Please email for information if interested in presenting a poster related to your work on causal inference.

List of Speakers:
Theme a)  Causality for Healthcare 
                   Miguel Hernan  -
                   Fredrik Johansson -
Theme b)  Learning Causal Dynamics from data 
                   David Jensen -
                   Olga Vitek -  
Agenda: We will shortly update the page with title and abstract for talks.
       1:00 - 1:30  Registration , Networking and Poster Setup
       1:30 - 1:45 pm   Brief Introduction by the organizers 
       Theme a : Causality for Healthcare
       1:45 -2:30 pm Miguel Hernan (40 min talk + 5 min Q&A)
       2:30- 3:15 pm Fredrik Johansson (40 min talk +5 min Q&A)
        Poster Session       
       3:15 - 4:15 pm - Poster/Networking Session + Snacks Break
      Theme b: Learning Causal Dynamics from data
      4:15- 5:00 pm -  David Jensen (40 min talk+ 5 min Q&A)
      5:00 - 5:45 pm - Olga Vitek  (40 min talk + 5 min Q&A).       
      5:45 - 6:00 pm - Closing Remarks