NeurIPS 2019 Workshop
“Do the right thing”: machine learning and causal inference for improved decision making
December 14, 2019, Vancouver, Vancouver Conventional Center, West Level 1, West Ballroom C
Workshop Summary
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.
Invited Speakers
Program Schedule
08:45am – 09:00am
West Ballroom C
Opening Remarks
09:00am – 09:30am
West Ballroom C
Confidence Intervals for Policy Evaluation in Adaptive Experiments
By Susan Athey
Invited speaker
09:30am – 10:00am
West Ballroom C
Optimal adjustment sets in non-parametric graphical models
Invited speaker
10:00am – 10:15am
West Ballroom C
Poster Spotlights
Two minute poster presentation
* Marie-Laure Charpignon, Elizabeth Manrao, Min Kang, Kirti Kamboj, Kamal Choudhary, John Hoegger – Moving beyond correlation: Using a causal inference approach to measure the impact of a customer’s attribute on product retention A case study on Microsoft Teams collaborative app
* Maja Rudolph – Multi-Cause Inference with Sequential Treatments
* Amanda Coston, Alexandra Chouldechova, Edward Kennedy – Counterfactual Risk Assessments, Evaluation, and Fairness
* Yuta Saito – Unbiased Pairwise Learning from Implicit Feedback
* Jeremy Yang , Dean Eckles , Paramveer Dhillon, Sinan Aral – Optimizing Targeting Policies via Sequential Experimentation for User Retention
* Alexander Markham, Moritz Grosse-Wentrup – Measurement Dependence Inducing Latent Causal Models
10:15am – 11:00am
West Ballroom C
Coffee break, posters, and 1-on-1 discussions
Poster Reminders
– Please make your posters 36W x 48H inches or 90 x 122 cm.
– Posters should be on lightweight paper, not laminated.
11:00am – 11:30am
West Ballroom C
Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time
By Susan Murphy
Invited speaker
11:30am – 12:00pm
West Ballroom C
Estimation and inference on high dimensional individualized treatment rule using split-and-pooled de-correlated score
By Ying-Qi Zhao
Invited speaker
12:00pm – 12:45pm
West Ballroom C
How machine learning, and causal inference work together: cross-pollination and new challenges
By David Sontag, Susan Athey, Susan Murphy, Andrea Rotnitzky, Ying-Qi Zhao, Susan Shortreed
Panel discussion
12:45pm – 02:30pm
West Ballroom C
Lunch
02:30pm – 03:00pm
West Ballroom C
Variable selection for causal inference: outcome-adaptive lasso
Invited speaker
03:00pm – 03:30pm
West Ballroom C
Oral Spotlights
Five minute oral presentation
* Jörn Boehnke, Pietro Bonaldi – Synthetic Regression Discontinuity: Estimating Treatment Effects using Machine Learning
* Yixin Wang, Dhanya Sridhar, David Blei – Equal Opportunity and Affirmative Action via Counterfactual Predictions
* Divyat Mahajan, Amit Sharma – Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
* Khashayar Khosravi, Vasilis Syrgkanis, Greg Lewis – Non-Parametric Inference Adaptive to Intrinsic Dimension
* Alexander D’Amour, Alexander Franks – Covariate Reduction for Weighted Causal Effect Estimation with Deconfounding Scores
03:30pm – 03:45pm
West Ballroom C
Poster Spotlights
Two minute poster presentation
* Rahul Singh, Liyang Sun – De-biased Machine Learning for Compliers
* Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russ Greiner – Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
* Jon Richens, Ciarán M. Lee, Saurabh Johri – Counterfactual diagnosis
* Jesse Krijthe, Tom Heskes – Estimating the long-term effect of early treatment initiation in Parkinson’s disease using observational data
* Bradley Butcher, Vincent S Huang, Jeremy Reffin, Sema K Sgaier, Grace Charles, Novi Quadrianto – Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world
* Victor Chernozhukov, Whitney Newey, Vira Semenova – Inference on the weighted average value function with high-dimensional state space
* Rahul Ladhania – A Sequence of Two Studies to Propose & Test Sub-groups with Heterogeneous Treatment Effects
* Miruna Oprescu, Vasilis Syrgkanis, Keith Battocchi – EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects
03:45pm – 04:30pm
West Ballroom C
Coffee break, posters, and 1-on-1 discussions
Poster Reminders
– Please make your posters 36W x 48H inches or 90 x 122 cm.
– Posters should be on lightweight paper, not laminated.
04:30pm – 04:45pm
West Ballroom C
Closing Remarks
Call for Papers
We solicit submissions of novel research related to all aspects of causal inference, counterfactual prediction, and autonomous action. This includes, but is not limited to, the following topics:
- Predicting counterfactual outcomes
- Reinforcement Learning and Causal Inference
- Causal transfer learning
- Mediation analysis
- Estimation of (conditional) average treatment effects
- Contextual bandit algorithms and on-policy learning
- Batch/offline learning from bandit feedback
- Off-policy evaluation and learning
- Interactive experimental control vs. counterfactual estimation from logged experiments
- Online A/B-testing vs. offline A/B-testing
- De-biasing observational data and feedback cycles
- Fairness of actions and causal aspects of fairness
- Applications in online systems (e.g. search, recommendation, ad placement)
- Applications in physical systems (e.g. cars, smart homes)
- Applications in medicine (e.g. personalized treatment, clinical trials)
We suggest extended abstracts of 2 pages in the NeurIPS format, but no specific format is enforced. The review process is double-blind. A maximum of 8 pages will be considered. References will not count towards the page limit. PDF files only. At the discretion of the organizers, accepted contributions will be assigned slots as contributed talks and others will be presented as posters.
Accepted Papers
Daniel L Chen – Deep IV in Law: Analysis of Appellate Impacts on Sentencing Using High-Dimensional Instrumental Variables
Jörn Boehnke, Pietro Bonaldi – Synthetic Regression Discontinuity: Estimating Treatment Effects using Machine Learning
Yixin Wang, Dhanya Sridhar, David Blei – Equal Opportunity and Affirmative Action via Counterfactual Predictions
Divyat Mahajan, Amit Sharma – Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Khashayar Khosravi, Vasilis Syrgkanis, Greg Lewis – Non-Parametric Inference Adaptive to Intrinsic Dimension
Alexander D’Amour, Alexander Franks – Covariate Reduction for Weighted Causal Effect Estimation with Deconfounding Scores
Steve Yadlowsky, Hongseok Namkoong, Sanjay Basu, John Duchi, Lu Tian – Bounds on the Conditional and Average Treatment Effect with Unobserved Confounding Factors
Marie-Laure Charpignon, Elizabeth Manrao, Min Kang, Kirti Kamboj, Kamal Choudhary, John Hoegger – Moving beyond correlation: Using a causal inference approach to measure the impact of a customer’s attribute on product retention A case study on Microsoft Teams collaborative app
Maja Rudolph – Multi-Cause Inference with Sequential Treatments
Amanda Coston, Alexandra Chouldechova, Edward Kennedy – Counterfactual Risk Assessments, Evaluation, and Fairness
Yuta Saito – Unbiased Pairwise Learning from Implicit Feedback
Jeremy Yang , Dean Eckles , Paramveer Dhillon, Sinan Aral – Optimizing Targeting Policies via Sequential Experimentation for User Retention
Alexander Markham, Moritz Grosse-Wentrup – Measurement Dependence Inducing Latent Causal Models
Théophile Griveau-Billion, Ben Calderhead – Efficient structure learning with automatic sparsity selection for causal graph processes
Rahul Singh, Liyang Sun – De-biased Machine Learning for Compliers
Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russ Greiner – Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
Jon Richens, Ciarán M. Lee, Saurabh Johri – Counterfactual diagnosis
Jesse Krijthe, Tom Heskes – Estimating the long-term effect of early treatment initiation in Parkinson’s disease using observational data
Bradley Butcher, Vincent S Huang, Jeremy Reffin, Sema K Sgaier, Grace Charles, Novi Quadrianto – Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world
Victor Chernozhukov, Whitney Newey, Vira Semenova – Inference on the weighted average value function with high-dimensional state space
Rahul Ladhania – A Sequence of Two Studies to Propose & Test Sub-groups with Heterogeneous Treatment Effects
Miruna Oprescu, Vasilis Syrgkanis, Keith Battocchi – EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari – Learning Good Interventions Sequentially via Causal Bandits
Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf – Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
Niranjani Prasad – Defining Admissible Rewards for High-Confidence Policy Evaluation
David Simchi-Levi, Yunzong Xu, Jinglong Zhao – Unifying Adaptivity and Switching Ability in Online Experimental Design
Dmytro Mykhaylov, David J Rohde, Flavian Vasile, Martin Bompaire, Olivier Jeunen – Three Methods for Training on Bandit Feedback
Finnian Lattimore, David J Rohde – Causal inference with Bayes rule
Chirag Modi, Uros Seljak – Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics
Negar Hassanpour, Russell Greiner – Learning Disentangled Representations for Counter Factual Regression
Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff – MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Yuta Saito, Gota Morishita – Dual Learning Algorithm for Delayed Feedback in Display Advertising
Shyngys Karimov, Gert Bijnens, Joep Konings – The impact of automatic wage indexation on Belgian employment, a machine learning approach
Jean Pouget-Abadie – Variance Reduction in Bipartite Experiments through Correlation Clustering
Jeroen Berrevoets, Sam Verboven, Wouter Verbeke – Optimising Individual-Treatment-Effect Using Bandits
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Chris J Pal, Yoshua Bengio – Fast Adaptation and Slow Meta-Learning of Neural Causal Models
Peter Wirnsberger, Jovana Mitrovic, Melissa Tan, Charles Blundell, Lars Buesing – Improved Decision Making in Structural Causal Bandits
Julius von Kügelgen, Paul Rubenstein, Bernhard Schölkopf, Adrian Weller – Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Logan Graham, Ciarán M. Lee, Yura Perov – Copy, paste, infer: A robust analysis of twin networks for counterfactual inference
Candice Schumann, Zhi T Lang, Nicholas Mattei, John P Dickerson – Group Fairness in Bandit Arm Selection
Anisha Zaveri, Victor Veitch – Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Anish Agarwal, Romain Cosson, Devavrat Shah, Dennis Shen – Bridging Randomized Control Trials and Personalized Treatments
Julie Josse, Imke Mayer, Jean-Philippe Vert – MissDeepCausal: causal inference from incomplete data using deep latent variable models
Victor Chernozhukov, Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis – Regularized estimation of high-dimensional semiparametric models
Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov – Semi-Parametric Efficient Policy Learning with Continuous Actions
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis – Heterogeneous Treatment Effects with Instruments
Suraj Nair, Yuke Zhu, Silvio Savarese, Li Fei-Fei – Visual Causal Induction for Goal Directed Tasks
Aahlad Manas Puli, Rajesh Ranganath – Generalized Control Functions for Flexible IV Estimation
Dhanya Sridhar, Victor Veitch, David Blei – Using Text Embeddings for Causal Inference
Nathan Kallus, Masatoshi Uehara – Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
Mohammadi Zaki, Avinash Mohan, Aditya Gopalan – Towards Optimal and Efficient Best Arm Identification in Linear Bandits
Issa Dahabreh – Causally interpretable meta-analysis with double/debiased machine learning
Yi Ding, Guillaume Basse, Panos Toulis – Minimax Crossover Designs
Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang – A Graph Autoencoder Approach to Causal Structure Learning
Alan Mishler, Niccolo Dalmasso – When the Oracle Misleads: Modeling the Consequences of Using Observable Rather than Potential Outcomes in Risk Assessment Instruments
Eli Sherman, David Arbour, Ilya Shpitser – Policy Interventions Under Interference
Duncan Wadsworth – Measuring Fairness in A/B Testing
Shuxi Zeng, Pengchuan Zhang, Denis Charles, Eren Manavoglu, Emre Kiciman – Robust Neural Network for Causal Invariant Features Extraction
Aleksander Wieczorek, Volker Roth – Information Theoretic Causal Effect Quantification
Shuxi Zeng, Murat Bayir, Denis Charles, Joseph J Pfeiffer, Emre Kiciman – Causal Transfer Random Forest: Leveraging Observational and Randomization Studies
Hao Liu, Anqi Liu, Tongxin Li, Saeed Karimi, Yisong Yue, Animashree Anandkumar – Disentangling Causal Effects from Latent Confounders using Interventions
Khizar Qureshi, Tauhid Zaman – Running Influence Campaigns on Twitter
Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur – Causal Inference with Selectively-Deconfounded Data
Jason S Hartford, Kevin Leyton-Brown – Identifying Valid Instruments via Effect Agreement
Organizers
Thorsten Joachims
Cornell University
Nathan Kallus
Cornell University
Adith Swaminathan
Microsoft Research
Michele Santacatterina
Cornell University
David Sontag
MIT
Angela Zhou
Cornell University