In recent years, machine learning has seen important advances in its theoretical and practical domains, with some of the most significant applications in online marketing and commerce, personalized medicine, and data-driven policy-making. This dramatic success has led to increased expectations for autonomous systems to make the right decision at the right target at the right time. This gives rise to one of the major challenges of machine learning today that is the understanding of the cause-effect connection. Indeed, actions, intervention, and decisions have important consequences, and so, in seeking to make the best decision, one must understand the process of identifying causality. By embracing causal reasoning autonomous systems will be able to answer counterfactual questions, such as “What if I had treated a patient differently?”, and “What if had ranked a list differently?” thus helping to establish the evidence base for important decision-making processes.
The purpose of this workshop is to bring together experts from different fields to discuss the relationships between machine learning and causal inference and to discuss and highlight the formalization and algorithmization of causality toward achieving human-level machine intelligence.
This purpose will guide the makeup of the invited talks and the topics for the panel discussions. The panel discussions will tackle controversial topics, with the intent of drawing out an engaging intellectual debate and conversation across fields.
This workshop will lead to advance and extend knowledge on how machine learning could be used to conduct causal inference, and how causal inference could support the development of machine learning methods for improved decision-making.
Author Information
Michele Santacatterina (TRIPODS Center for Data Science - Cornell University)
Thorsten Joachims (Cornell)
Nathan Kallus (Cornell University)
Adith Swaminathan (Microsoft Research)
David Sontag (MIT)
Angela Zhou (Cornell University)
More from the Same Authors
-
2019 Workshop: Machine Learning with Guarantees »
Ben London · Gintare Karolina Dziugaite · Daniel Roy · Thorsten Joachims · Aleksander Madry · John Shawe-Taylor -
2019 Poster: The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric »
Nathan Kallus · Angela Zhou -
2019 Poster: Policy Learning for Fairness in Ranking »
Ashudeep Singh · Thorsten Joachims -
2019 Poster: Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds »
Nathan Kallus · Angela Zhou -
2019 Poster: Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning »
Nathan Kallus · Masatoshi Uehara -
2019 Poster: Policy Evaluation with Latent Confounders via Optimal Balance »
Andrew Bennett · Nathan Kallus -
2019 Poster: Deep Generalized Method of Moments for Instrumental Variable Analysis »
Andrew Bennett · Nathan Kallus · Tobias Schnabel -
2018 Workshop: Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy »
Manuela Veloso · Nathan Kallus · Sameena Shah · Senthil Kumar · Isabelle Moulinier · Jiahao Chen · John Paisley -
2018 Poster: Causal Inference with Noisy and Missing Covariates via Matrix Factorization »
Nathan Kallus · Xiaojie Mao · Madeleine Udell -
2018 Poster: Removing Hidden Confounding by Experimental Grounding »
Nathan Kallus · Aahlad Manas Puli · Uri Shalit -
2018 Spotlight: Removing Hidden Confounding by Experimental Grounding »
Nathan Kallus · Aahlad Manas Puli · Uri Shalit -
2018 Poster: Confounding-Robust Policy Improvement »
Nathan Kallus · Angela Zhou -
2018 Poster: Balanced Policy Evaluation and Learning »
Nathan Kallus -
2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
Ricardo Silva · Panagiotis Toulis · John Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan -
2017 Poster: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
2017 Oral: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
Ricardo Silva · John Shawe-Taylor · Adith Swaminathan · Thorsten Joachims -
2015 Poster: The Self-Normalized Estimator for Counterfactual Learning »
Adith Swaminathan · Thorsten Joachims -
2015 Spotlight: The Self-Normalized Estimator for Counterfactual Learning »
Adith Swaminathan · Thorsten Joachims -
2013 Poster: Learning Trajectory Preferences for Manipulators via Iterative Improvement »
Ashesh Jain · Brian Wojcik · Thorsten Joachims · Ashutosh Saxena -
2011 Poster: Semantic Labeling of 3D Point Clouds for Indoor Scenes »
Hema Koppula · Abhishek Anand · Thorsten Joachims · Ashutosh Saxena -
2007 Workshop: Machine Learning for Web Search »
Denny Zhou · Olivier Chapelle · Thorsten Joachims · Thomas Hofmann
