CRT 2019

We are happy to announce the first Bridging causal inference, reinforcement learning and transfer learning (CRT2019) workshop on Thursday, September 19th 2019 at the MIT Samberg center, 6th Floor, rooms 5-6. This workshop is part of the IBM Research's AI week, registration is free but mandatory .

Causal inference is an increasingly popular research direction, focused on discovering causal relations from data and exploiting them to predict the effect of actions/interventions in a system. Recently, there has been exciting new works pointing at connections between causal inference and two other important fields of machine learning: reinforcement learning and transfer learning. Although there seems to be a natural connection between these fields, the different research communities are still separate, a situation complicated by the different terminologies and assumptions. In this workshop we will bring together these different communities in the context of causal inference. We will include speakers from academia and industry that have been pioneering research in these intersections.

Schedule

9:00-9:20 AM	Breakfast and poster setup
9:20-9:30 AM	Introduction
9:30-10:30 AM	Elias Bareinboim (Columbia): Towards Causal Reinforcement Learning
10:30-11:00 AM	Coffee and poster session
11:00-12:00 AM	David Sontag (MIT): Checking Assumptions in Causal Inference from Observational Data
12:00-1:00 PM	Lunch (not provided) and poster session
1:00-2:00 PM	Kun Zhang (CMU): Causality, independence, and adaptive prediction
2:00-3:00 PM	Coffee and poster session

Invited Speakers

Elias Bareinboim

Associate Professor,
Columbia
David Sontag

Associate Professor,
MIT
Kun Zhang

Assistant Professor,
CMU

Posters

Mark Feblowitz, Oktie Hassanzadeh and Kavitha Srinivas: Extracting Causal Knowledge from Text to Automate Planning Domain Creation
Xinkun Nie, Emma Brunskill and Stefan Wager: Learning When-to-Treat Policies
Yue Yu, Jie Chen, Tian Gao and Mo Yu: DAG-GNN: DAG Structure Learning with Graph Neural Networks
Debarun Bhattacharjya, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney and Dharmashankar Subramanian: Event-Driven Continuous Time Bayesian Networks
Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera and Guy Bresler: Sample Efficient Active Learning of Causal Trees
Tian Gao and Dennis Wei: Parallel Bayesian Network Structure Learning
Fredrik Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel Brat, David Sontag and Kush Varshney: Characterization of Overlap in Observational Studies
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Murat Kocaoglu, Karthikeyan Shanmugam, Amin Jaber and Elias Bareinboim: Characaterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Anish Agarwal, Romain Cosson, Devavrat Shah and Dennis Shen: Bridging Randomized Control Trials and Personalized Treatments
Akanksha Atrey, Kaleigh Clary and David Jensen: Evaluating Saliency Maps for Deep RL Using Counterfactual Reasoning
Chandler Squires, Daniel Bernstein, Caroline Uhler and Basil Saeed: Ordering-Based Learning of Causal Structures with Latent Variables
Zenna Tavares, James Koppel, Xin Zhang and Armando Solar-Lezama: A Language for Counterfactual Generative Models
Rahul Singh and Liyang Sun: De-biased Machine Learning for Compliers
Michael Oberst and David Sontag: Counterfactual Policy Introspection using Structural Causal Models

Organizing Committee


Sara Magliacane	Karthikeyan Shanmugam	Kristjan Greenewald

Murat Kocaoglu	Dmitriy Katz-Rogozhnikov

Call for Posters

We invite researchers to submit work in (but not limited to) the following areas:

Model-based RL with links to causal inference and counterfactual reasoning, e.g. causal bandits, off-policy evaluation and learning, counterfactual estimation from logged experiments, batch/offline learning from bandit feedback.
Active causal learning, e.g. experiment design for learning causal structures or effects.
Transportability, combining different experimental and observational data, especially in conjunction with transfer learning or RL settings.
Causal transfer learning, applications of causal reasoning (e.g. invariances and conditional independences) to transfer learning.
Estimation of (conditional) average treatment effects.

Submissions

Submission can be made via an EasyChair submission. Submissions of new ideas, recently published works and/or extension of existing works are welcome. Parallel submissions or submissions of under-review works are also permitted. The submission can either be a previously submitted conference or workshop paper, or an extended abstract of 1 to 3 pages (excluding references) in PDF format using NeurIPS style. Author names do not need to be anonymized. Submissions will be accepted as poster presentations. The final versions will be posted on the workshop website (and are archival but do not constitute a proceeding).

Key Dates

Submission deadline: September 9, 2019.
Notification date: September 10, 2019.
Meeting Date: September 19, 2019.

Attendance

For each accepted poster, at least one author must attend the workshop and present the poster.

Elias Bareinboim

David Sontag

Kun Zhang

Organizing Committee

Submissions

Key Dates

Attendance