Causality and Machine Learning

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Preprint Project

Towards Modular Machine Learning Pipelines

Aditya Modi, Jivat Neet Kaur, Maggie Makar, Pavan Mallapragada, Amit Sharma, Emre Kiciman, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Publication

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

ICLR 2023 | May 2023

Publication Project

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan

April 2023

Publication

Counterfactual (Non-)identifiability of Learned Structural Causal Models

Arash Nasr-Esfahany, Emre Kiciman

January 2023

Publication

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

International Conference on Learning Representations (ICLR) | January 2023

Notable Top-25% of accepted papers

Publication Project

Invariant Language Modeling

Maxim Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West

2022 Empirical Methods in Natural Language Processing | December 2022

Publication

A Causal AI Suite for Decision-Making

Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022

Publication Project Project Project

Membership Inference Attacks and Generalization: A Causal Perspective

Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena

Computer and Communications Security (CCS) | November 2022

Publication Project

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramainan, Amit Sharma

ICML 2022 | July 2022

Publication Project

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

Workshop on Spurious Correlations, Invariance, and Stability, ICML 2022 | July 2022

Spotlight

Publication Project

Causal Inference in the Presence of Interference in Sponsored Search Advertising

Razieh Nabi, Joel Pfeiffer, Denis Charles, Emre Kiciman

Frontiers in Big Data | June 2022, Vol 5

Publication

Optimal Transport for Causal Discovery

Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

ICLR 2022 | April 2022

Publication Project

Simultaneous Missing Value Imputation and Structure Learning with Groups

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang

2022 Neural Information Processing Systems | February 2022

Publication Publication Project

Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Saloni Dash, Vineeth Balasubramanian, Amit Sharma

Winter Conference on Applications of Computer Vision (WACV) | January 2022

PDF Publication Project

Local Constraint-Based Causal Discovery under Selection Bias

Philip Versteeg, Cheng Zhang, Joris Mooij

Conference on Causal Learning and Reasoning (CLeaR) | January 2022

Publication Project

Heuristic-Guided Reinforcement Learning

Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

NeurIPS 2021 | December 2021

Publication

Formation of Social Ties Predicts Food Choice: A Campus-wide Longitudinal Study

Kristina Gligorić, Ryen W. White, Emre Kiciman, Eric Horvitz, Arnaud Chiolero, Robert West

CM Conference on Computer-Supported Cooperative Work and Social Computing CSCW | October 2021

Honorable Mention

Publication

Clustered into control: Heterogeneous causal impacts of water infrastructure failure

Brandon Cunningham, Casey J. Wichman

Economic Inquiry | October 2021

DOI Publication Publication Publication

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

International Conference on Machine Learning (ICML) | July 2021

Publication Github Project Project

Causally Constrained Data Synthesis for Private Data Release

Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople

Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021

PDF

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

PDF Publication Project Project

Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

Shuxi Zeng, Murat Ali Bayir, Joel Pfeiffer, Denis Charles, Emre Kiciman

14th ACM Intl Conf. on Web Search and Data Mining (WSDM) | March 2021

Publication

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman

ACM Intl. Conf. on Web Search and Data Mining (WSDM) | March 2021

PDF Publication Publication Project

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

AAAI Conference on Artificial Intelligence (AAAI) | February 2021

Publication PDF Project

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix

Ruocheng Guo, Pengchuan Zhang, Hao Liu, Emre Kiciman

January 2021

arXiv

Publication

The Connection between Out-of-Distribution Generalization and Privacy of ML Models

Divyat Mahajan, Shruti Tople, Amit Sharma

Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020

PDF

DoWhy: An End-to-End Library for Causal Inference

Amit Sharma, Emre Kiciman

November 2020

Causal Data Science Meeting (https://causalscience.org/)

Publication Video Github Project

Multi-task Causal Learning with Gaussian Processes

Virgina Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González

Neural Information Processing Systems (NeurIPS 2020) | October 2020

Publication

Provably Good Batch Reinforcement Learning Without Great Exploration

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

2020 Neural Information Processing Systems | October 2020

Publication Project

Debiasing Item-to-Item Recommendations With Small Annotated Datasets

Tobias Schnabel, Paul Bennett

RecSys | September 2020

PDF Github

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya V. Nori

International Conference on Machine Learning (ICML) | July 2020

Publication Github Project Project

Learning Calibratable Policies using Programmatic Style-Consistency

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht

2020 International Conference on Machine Learning | July 2020

Publication PDF Project

Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

Koustuv Saha, Amit Sharma

AAAI International Conference on Web and Social Media (ICWSM) | June 2020

PDF Project

Reliable and Efficient Anytime Skeleton Learning

Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang

To appear in proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20) | February 2020

Publication Project

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) | February 2020

PDF Publication

Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

ACM Conference on Fairness, Accountability, and Transparency | January 2020

PDF Project Project

Causal Transfer Random Forest: Leveraging Observational and Randomization Studies

Shuxi Zeng, Emre Kiciman, Denis Charles, Joel Pfeiffer, Murat Ali Bayir

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Robust Neural Networks for Causal Invariant Features Extraction

Shuxi Zeng, Pengchuan Zhang, Denis Charles, Eren Manavoglu, Emre Kiciman

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Deep Generalized Method of Moments for Instrumental Variable Analysis

Tobias Schnabel, Andrew Bennett, Nathan Kallus

NeurIPS | December 2019

Author's Version

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Divyat Mahajan, Chenhao Tan, Amit Sharma

CausalML: Machine Learning and Causal Inference for Improved Decision Making Workshop, NeurIPS 2019 | December 2019

Publication Project

A Causal View on Robustness of Neural Networks

Cheng Zhang, Yingzhen Li

June 2019

Publication Project

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang

June 2019

Publication Project

Causal Discovery in the Presence of Missing Data

Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang

International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019

Editor(s): Kamalika Chaudhuri, Masashi Sugiyama

Publication Project

A Distillation Approach to Data Efficient Individual Treatment Effect Estimation

Maggie Makar, Adith Swaminathan, Emre Kiciman

AAAI-19 | January 2019

PDF

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Amit Sharma

December 2018

Publication Project

Split-door criterion: Identification of causal effects through auxiliary outcomes

Amit Sharma, Jake Hofman, Duncan Watts

Annals of Applied Statistics | December 2018, Vol 12(4): pp. 2699-2733

Publication

Tutorial on Causal Inference and Counterfactual Reasoning

Emre Kiciman, Amit Sharma

ACM KDD International Conference on Knowledge Discovery and Data Mining | August 2018

Publication Project

Deep Learning with Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

2018 International Conference on Learning Representations | May 2018

Publication Publication Project

What is the Causal Effect of Knowledge on Preferences

Katherine Simpson Simpson, Mikolaj Czajkowski, Nick Hanley, Jacob LaRiviere

Resource and Energy Economics | February 2018

Publication Publication

Off-Policy Evaluation For Slate Recommendation

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni

2017 Neural Information Processing Systems | December 2017

Publication Project

Harvesting Randomness to Optimize Distributed Systems

Mathias Lecuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins

16th ACM Workshop on Hot Topics in Networks (HotNets) | November 2017

Publication

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing | February 2017

ISBN: 978-1-4503-4335-0/17/03

Publication Publication Download Project

Prediction and explanation in social systems

Jake Hofman, Amit Sharma, Duncan Watts

Science | February 2017, Vol 355: pp. 486-488

Publication Publication Publication

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

2016 International Conference on Machine Learning | June 2016

Publication

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Preprint Project

Towards Modular Machine Learning Pipelines

Aditya Modi, Jivat Neet Kaur, Maggie Makar, Pavan Mallapragada, Amit Sharma, Emre Kiciman, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Publication

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

ICLR 2023 | May 2023

Publication Project

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan

April 2023

Publication

Counterfactual (Non-)identifiability of Learned Structural Causal Models

Arash Nasr-Esfahany, Emre Kiciman

January 2023

Publication

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

International Conference on Learning Representations (ICLR) | January 2023

Notable Top-25% of accepted papers

Publication Project

Invariant Language Modeling

Maxim Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West

2022 Empirical Methods in Natural Language Processing | December 2022

Publication

A Causal AI Suite for Decision-Making

Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022

Publication Project Project Project

Membership Inference Attacks and Generalization: A Causal Perspective

Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena

Computer and Communications Security (CCS) | November 2022

Publication Project

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramainan, Amit Sharma

ICML 2022 | July 2022

Publication Project

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

Workshop on Spurious Correlations, Invariance, and Stability, ICML 2022 | July 2022

Spotlight

Publication Project

Causal Inference in the Presence of Interference in Sponsored Search Advertising

Razieh Nabi, Joel Pfeiffer, Denis Charles, Emre Kiciman

Frontiers in Big Data | June 2022, Vol 5

Publication

Optimal Transport for Causal Discovery

Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

ICLR 2022 | April 2022

Publication Project

Simultaneous Missing Value Imputation and Structure Learning with Groups

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang

2022 Neural Information Processing Systems | February 2022

Publication Publication Project

Local Constraint-Based Causal Discovery under Selection Bias

Philip Versteeg, Cheng Zhang, Joris Mooij

Conference on Causal Learning and Reasoning (CLeaR) | January 2022

Publication Project

Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Saloni Dash, Vineeth Balasubramanian, Amit Sharma

Winter Conference on Applications of Computer Vision (WACV) | January 2022

PDF Publication Project

Heuristic-Guided Reinforcement Learning

Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

NeurIPS 2021 | December 2021

Publication

Formation of Social Ties Predicts Food Choice: A Campus-wide Longitudinal Study

Kristina Gligorić, Ryen W. White, Emre Kiciman, Eric Horvitz, Arnaud Chiolero, Robert West

CM Conference on Computer-Supported Cooperative Work and Social Computing CSCW | October 2021

Honorable Mention

Publication

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

International Conference on Machine Learning (ICML) | July 2021

Publication Github Project Project

Causally Constrained Data Synthesis for Private Data Release

Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople

Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021

PDF

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

PDF Publication Project Project

Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

Shuxi Zeng, Murat Ali Bayir, Joel Pfeiffer, Denis Charles, Emre Kiciman

14th ACM Intl Conf. on Web Search and Data Mining (WSDM) | March 2021

Publication

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman

ACM Intl. Conf. on Web Search and Data Mining (WSDM) | March 2021

PDF Publication Publication Project

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

AAAI Conference on Artificial Intelligence (AAAI) | February 2021

Publication PDF Project

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix

Ruocheng Guo, Pengchuan Zhang, Hao Liu, Emre Kiciman

January 2021

arXiv

Publication

The Connection between Out-of-Distribution Generalization and Privacy of ML Models

Divyat Mahajan, Shruti Tople, Amit Sharma

Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020

PDF

DoWhy: An End-to-End Library for Causal Inference

Amit Sharma, Emre Kiciman

November 2020

Causal Data Science Meeting (https://causalscience.org/)

Publication Video Github Project

Multi-task Causal Learning with Gaussian Processes

Virgina Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González

Neural Information Processing Systems (NeurIPS 2020) | October 2020

Publication

Provably Good Batch Reinforcement Learning Without Great Exploration

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

2020 Neural Information Processing Systems | October 2020

Publication Project

Debiasing Item-to-Item Recommendations With Small Annotated Datasets

Tobias Schnabel, Paul Bennett

RecSys | September 2020

PDF Github

Learning Calibratable Policies using Programmatic Style-Consistency

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht

2020 International Conference on Machine Learning | July 2020

Publication PDF Project

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya V. Nori

International Conference on Machine Learning (ICML) | July 2020

Publication Github Project Project

Reliable and Efficient Anytime Skeleton Learning

Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang

To appear in proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20) | February 2020

Publication Project

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) | February 2020

PDF Publication

Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

ACM Conference on Fairness, Accountability, and Transparency | January 2020

PDF Project Project

Robust Neural Networks for Causal Invariant Features Extraction

Shuxi Zeng, Pengchuan Zhang, Denis Charles, Eren Manavoglu, Emre Kiciman

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Causal Transfer Random Forest: Leveraging Observational and Randomization Studies

Shuxi Zeng, Emre Kiciman, Denis Charles, Joel Pfeiffer, Murat Ali Bayir

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Divyat Mahajan, Chenhao Tan, Amit Sharma

CausalML: Machine Learning and Causal Inference for Improved Decision Making Workshop, NeurIPS 2019 | December 2019

Publication Project

Deep Generalized Method of Moments for Instrumental Variable Analysis

Tobias Schnabel, Andrew Bennett, Nathan Kallus

NeurIPS | December 2019

Author's Version

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang

June 2019

Publication Project

A Causal View on Robustness of Neural Networks

Cheng Zhang, Yingzhen Li

June 2019

Publication Project

Causal Discovery in the Presence of Missing Data

Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang

International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019

Editor(s): Kamalika Chaudhuri, Masashi Sugiyama

Publication Project

A Distillation Approach to Data Efficient Individual Treatment Effect Estimation

Maggie Makar, Adith Swaminathan, Emre Kiciman

AAAI-19 | January 2019

PDF

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Amit Sharma

December 2018

Publication Project

Split-door criterion: Identification of causal effects through auxiliary outcomes

Amit Sharma, Jake Hofman, Duncan Watts

Annals of Applied Statistics | December 2018, Vol 12(4): pp. 2699-2733

Publication

Tutorial on Causal Inference and Counterfactual Reasoning

Emre Kiciman, Amit Sharma

ACM KDD International Conference on Knowledge Discovery and Data Mining | August 2018

Publication Project

Deep Learning with Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

2018 International Conference on Learning Representations | May 2018

Publication Publication Project

Off-Policy Evaluation For Slate Recommendation

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni

2017 Neural Information Processing Systems | December 2017

Publication Project

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing | February 2017

ISBN: 978-1-4503-4335-0/17/03

Publication Publication Download Project

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

2016 International Conference on Machine Learning | June 2016

Publication

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Preprint Project

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Harvesting Randomness to Optimize Distributed Systems

Mathias Lecuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins

16th ACM Workshop on Hot Topics in Networks (HotNets) | November 2017

Publication

Invariant Language Modeling

Maxim Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West

2022 Empirical Methods in Natural Language Processing | December 2022

Publication

Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

Koustuv Saha, Amit Sharma

AAAI International Conference on Web and Social Media (ICWSM) | June 2020

PDF Project

Membership Inference Attacks and Generalization: A Causal Perspective

Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena

Computer and Communications Security (CCS) | November 2022

Publication Project

Causally Constrained Data Synthesis for Private Data Release

Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople

Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021

PDF

The Connection between Out-of-Distribution Generalization and Privacy of ML Models

Divyat Mahajan, Shruti Tople, Amit Sharma

Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020

PDF

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya V. Nori

International Conference on Machine Learning (ICML) | July 2020

Publication Github Project Project

Causal Inference in the Presence of Interference in Sponsored Search Advertising

Razieh Nabi, Joel Pfeiffer, Denis Charles, Emre Kiciman

Frontiers in Big Data | June 2022, Vol 5

Publication

Deep Learning with Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

2018 International Conference on Learning Representations | May 2018

Publication Publication Project

Off-Policy Evaluation For Slate Recommendation

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni

2017 Neural Information Processing Systems | December 2017

Publication Project

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing | February 2017

ISBN: 978-1-4503-4335-0/17/03

Publication Publication Download Project

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

2016 International Conference on Machine Learning | June 2016

Publication

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

Clustered into control: Heterogeneous causal impacts of water infrastructure failure

Brandon Cunningham, Casey J. Wichman

Economic Inquiry | October 2021

DOI Publication Publication Publication

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Amit Sharma

December 2018

Publication Project

What is the Causal Effect of Knowledge on Preferences

Katherine Simpson Simpson, Mikolaj Czajkowski, Nick Hanley, Jacob LaRiviere

Resource and Energy Economics | February 2018

Publication Publication

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

International Conference on Machine Learning (ICML) | July 2021

Publication Github Project Project

Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

Koustuv Saha, Amit Sharma

AAAI International Conference on Web and Social Media (ICWSM) | June 2020

PDF Project

Reliable and Efficient Anytime Skeleton Learning

Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang

To appear in proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20) | February 2020

Publication Project

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing | February 2017

ISBN: 978-1-4503-4335-0/17/03

Publication Publication Download Project

Prediction and explanation in social systems

Jake Hofman, Amit Sharma, Duncan Watts

Science | February 2017, Vol 355: pp. 486-488

Publication Publication Publication

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Preprint Project

Towards Modular Machine Learning Pipelines

Aditya Modi, Jivat Neet Kaur, Maggie Makar, Pavan Mallapragada, Amit Sharma, Emre Kiciman, Adith Swaminathan

2023 International Conference on Machine Learning | July 2023

Publication

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

ICLR 2023 | May 2023

Publication Project

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

International Conference on Learning Representations (ICLR) | January 2023

Notable Top-25% of accepted papers

Publication Project

Invariant Language Modeling

Maxim Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Saurabh Tiwary, Robert West

2022 Empirical Methods in Natural Language Processing | December 2022

Publication

A Causal AI Suite for Decision-Making

Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022

Publication Project Project Project

Membership Inference Attacks and Generalization: A Causal Perspective

Teodora Baluta, Shiqi Shen, S. Hitarth, Shruti Tople, Prateek Saxena

Computer and Communications Security (CCS) | November 2022

Publication Project

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramainan, Amit Sharma

ICML 2022 | July 2022

Publication Project

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma

Workshop on Spurious Correlations, Invariance, and Stability, ICML 2022 | July 2022

Spotlight

Publication Project

Optimal Transport for Causal Discovery

Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

ICLR 2022 | April 2022

Publication Project

Simultaneous Missing Value Imputation and Structure Learning with Groups

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang

2022 Neural Information Processing Systems | February 2022

Publication Publication Project

Local Constraint-Based Causal Discovery under Selection Bias

Philip Versteeg, Cheng Zhang, Joris Mooij

Conference on Causal Learning and Reasoning (CLeaR) | January 2022

Publication Project

Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Saloni Dash, Vineeth Balasubramanian, Amit Sharma

Winter Conference on Applications of Computer Vision (WACV) | January 2022

PDF Publication Project

Heuristic-Guided Reinforcement Learning

Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

NeurIPS 2021 | December 2021

Publication

Formation of Social Ties Predicts Food Choice: A Campus-wide Longitudinal Study

Kristina Gligorić, Ryen W. White, Emre Kiciman, Eric Horvitz, Arnaud Chiolero, Robert West

CM Conference on Computer-Supported Cooperative Work and Social Computing CSCW | October 2021

Honorable Mention

Publication

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma

International Conference on Machine Learning (ICML) | July 2021

Publication Github Project Project

Causally Constrained Data Synthesis for Private Data Release

Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople

Distributed and Private Machine Learning (DPML) Workshop at ICLR’21 | May 2021

PDF

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, Amit Sharma

AAAI/ACM Conference on AI, Ethics, and Society (AIES) | April 2021

PDF Publication Project Project

Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

Shuxi Zeng, Murat Ali Bayir, Joel Pfeiffer, Denis Charles, Emre Kiciman

14th ACM Intl Conf. on Web Search and Data Mining (WSDM) | March 2021

Publication

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman

ACM Intl. Conf. on Web Search and Data Mining (WSDM) | March 2021

PDF Publication Publication Project

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Alfonso Amayeulas, Amit Deshpande, Amit Sharma

AAAI Conference on Artificial Intelligence (AAAI) | February 2021

Publication PDF Project

The Connection between Out-of-Distribution Generalization and Privacy of ML Models

Divyat Mahajan, Shruti Tople, Amit Sharma

Privacy Preserving Machine Learning (PriML/PPML) Workshop at NeurIPS’20 | December 2020

PDF

Multi-task Causal Learning with Gaussian Processes

Virgina Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González

Neural Information Processing Systems (NeurIPS 2020) | October 2020

Publication

Provably Good Batch Reinforcement Learning Without Great Exploration

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

2020 Neural Information Processing Systems | October 2020

Publication Project

Debiasing Item-to-Item Recommendations With Small Annotated Datasets

Tobias Schnabel, Paul Bennett

RecSys | September 2020

PDF Github

Learning Calibratable Policies using Programmatic Style-Consistency

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht

2020 International Conference on Machine Learning | July 2020

Publication PDF Project

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya V. Nori

International Conference on Machine Learning (ICML) | July 2020

Publication Github Project Project

Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

Koustuv Saha, Amit Sharma

AAAI International Conference on Web and Social Media (ICWSM) | June 2020

PDF Project

Reliable and Efficient Anytime Skeleton Learning

Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang

To appear in proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20) | February 2020

Publication Project

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) | February 2020

PDF Publication

Explaining Machine Learning Classifiers through Diverse Counterfactual Examples

Ramaravind K. Mothilal, Amit Sharma, Chenhao Tan

ACM Conference on Fairness, Accountability, and Transparency | January 2020

PDF Project Project

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Divyat Mahajan, Chenhao Tan, Amit Sharma

CausalML: Machine Learning and Causal Inference for Improved Decision Making Workshop, NeurIPS 2019 | December 2019

Publication Project

Deep Generalized Method of Moments for Instrumental Variable Analysis

Tobias Schnabel, Andrew Bennett, Nathan Kallus

NeurIPS | December 2019

Author's Version

Causal Discovery in the Presence of Missing Data

Cheng Zhang, Ruibo Tu, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang

International Conference on Artificial Intelligence and Statistics (AISTATS) | April 2019

Editor(s): Kamalika Chaudhuri, Masashi Sugiyama

Publication Project

A Distillation Approach to Data Efficient Individual Treatment Effect Estimation

Maggie Makar, Adith Swaminathan, Emre Kiciman

AAAI-19 | January 2019

PDF

Tutorial on Causal Inference and Counterfactual Reasoning

Emre Kiciman, Amit Sharma

ACM KDD International Conference on Knowledge Discovery and Data Mining | August 2018

Publication Project

Deep Learning with Logged Bandit Feedback

Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

2018 International Conference on Learning Representations | May 2018

Publication Publication Project

Off-Policy Evaluation For Slate Recommendation

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni

2017 Neural Information Processing Systems | December 2017

Publication Project

Harvesting Randomness to Optimize Distributed Systems

Mathias Lecuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins

16th ACM Workshop on Hot Topics in Networks (HotNets) | November 2017

Publication

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

Alexandra Olteanu, Onur Varol, Emre Kiciman

Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing | February 2017

ISBN: 978-1-4503-4335-0/17/03

Publication Publication Download Project

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

2016 International Conference on Machine Learning | June 2016

Publication

Estimating the Causal Impact of Recommendation Systems from Observational Data

Amit Sharma, Jake Hofman, Duncan Watts

Proceedings of the Sixteenth ACM Conference on Economics and Computation | June 2015

ISBN: 978-1-4503-3410-5

Publication Publication

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan

April 2023

Publication

Counterfactual (Non-)identifiability of Learned Structural Causal Models

Arash Nasr-Esfahany, Emre Kiciman

January 2023

Publication

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang

June 2019

Publication Project

A Causal View on Robustness of Neural Networks

Cheng Zhang, Yingzhen Li

June 2019

Publication Project

Causal Inference in the Presence of Interference in Sponsored Search Advertising

Razieh Nabi, Joel Pfeiffer, Denis Charles, Emre Kiciman

Frontiers in Big Data | June 2022, Vol 5

Publication

Clustered into control: Heterogeneous causal impacts of water infrastructure failure

Brandon Cunningham, Casey J. Wichman

Economic Inquiry | October 2021

DOI Publication Publication Publication

The effects of remote work on collaboration among information workers

Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jeffrey Weston, Connor Joyce, Neha Parikh Shah, Kevin Sherman, Brent Hecht, Jaime Teevan

Nature Human Behaviour | September 2021

Publication Project Project

Split-door criterion: Identification of causal effects through auxiliary outcomes

Amit Sharma, Jake Hofman, Duncan Watts

Annals of Applied Statistics | December 2018, Vol 12(4): pp. 2699-2733

Publication

What is the Causal Effect of Knowledge on Preferences

Katherine Simpson Simpson, Mikolaj Czajkowski, Nick Hanley, Jacob LaRiviere

Resource and Energy Economics | February 2018

Publication Publication

Prediction and explanation in social systems

Jake Hofman, Amit Sharma, Duncan Watts

Science | February 2017, Vol 355: pp. 486-488

Publication Publication Publication

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix

Ruocheng Guo, Pengchuan Zhang, Hao Liu, Emre Kiciman

January 2021

arXiv

Publication

DoWhy: An End-to-End Library for Causal Inference

Amit Sharma, Emre Kiciman

November 2020

Causal Data Science Meeting (https://causalscience.org/)

Publication Video Github Project

Robust Neural Networks for Causal Invariant Features Extraction

Shuxi Zeng, Pengchuan Zhang, Denis Charles, Eren Manavoglu, Emre Kiciman

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Causal Transfer Random Forest: Leveraging Observational and Randomization Studies

Shuxi Zeng, Emre Kiciman, Denis Charles, Joel Pfeiffer, Murat Ali Bayir

December 2019

NeurIPS 2019 Workshop, “Do the right thing”: machine learning and causal inference for improved decision making

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Amit Sharma

December 2018

Publication Project