Causality and Machine Learning

出版物

表示方法:

- Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
  
  Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- Robust Root Cause Diagnosis using In-Distribution Interventions
  
  Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- DEDUCE: Deductive Consistency as a Frame Work to Evaluate LLM Reasoning
  
  Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  PDF
- Re-Imagine: Symbolic Benchmark Synthesis for Reasoning Evaluation
  
  Xinnuo Xu, Rachel Lawrence, Kshitij Dubey, Atharva Pandey, Fabian Falck, Risa Ueno, Aditya Nori, Rahul Sharma, Amit Sharma, Javier González
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  Publication PDF
- Teaching Transformers Causal Reasoning through Axiomatic Training
  
  Aniket Vashishtha, Abhinav Kumar, Abbavaram Gowtham Reddy, Kabir Ahuja, Vineeth N. Balasubramanian, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  DOI Publication Publication
- Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
  
  Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  Publication
- Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
  
  Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan
  
  Transactions on Machine Learning Research (TMLR) | August 2024
  
  Featured Certification
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint プロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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, 巻5
  
  Publication
- Optimal Transport for Causal Discovery
  
  Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
  
  ICLR 2022 | April 2022
  
  Publication プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- Local Constraint-Based Causal Discovery under Selection Bias
  
  Philip Versteeg, Cheng Zhang, Joris Mooij
  
  Conference on Causal Learning and Reasoning (CLeaR) | January 2022
  
  Publication プロジェクト
- 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 プロジェクトプロジェクト
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github プロジェクトプロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 動画 Github プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 Github プロジェクトプロジェクト
- 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 プロジェクト
- A Causal View on Robustness of Neural Networks
  
  Cheng Zhang, Yingzhen Li
  
  June 2019
  
  Publication プロジェクト
- Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
  
  Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
  
  June 2019
  
  Publication プロジェクト
- 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
  
  編集者: Kamalika Chaudhuri, Masashi Sugiyama
  
  Publication プロジェクト
- 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 プロジェクト
- Split-door criterion: Identification of causal effects through auxiliary outcomes
  
  Amit Sharma, Jake Hofman, Duncan Watts
  
  Annals of Applied Statistics | December 2018, 巻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 プロジェクト
- Deep Learning with Logged Bandit Feedback
  
  Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
  
  2018 International Conference on Learning Representations | May 2018
  
  Publication Publication プロジェクト
- 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 プロジェクト
- 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
  
  公開日公開日ダウンロードプロジェクト
- Prediction and explanation in social systems
  
  Jake Hofman, Amit Sharma, Duncan Watts
  
  Science | February 2017, 巻355: pp. 486-488
  
  公開日公開日公開日
- 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
  
  公開日公開日

- Robust Root Cause Diagnosis using In-Distribution Interventions
  
  Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
  
  Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- Re-Imagine: Symbolic Benchmark Synthesis for Reasoning Evaluation
  
  Xinnuo Xu, Rachel Lawrence, Kshitij Dubey, Atharva Pandey, Fabian Falck, Risa Ueno, Aditya Nori, Rahul Sharma, Amit Sharma, Javier González
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  Publication PDF
- DEDUCE: Deductive Consistency as a Frame Work to Evaluate LLM Reasoning
  
  Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  PDF
- Teaching Transformers Causal Reasoning through Axiomatic Training
  
  Aniket Vashishtha, Abhinav Kumar, Abbavaram Gowtham Reddy, Kabir Ahuja, Vineeth N. Balasubramanian, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  DOI Publication Publication
- Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
  
  Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  Publication
- Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
  
  Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan
  
  Transactions on Machine Learning Research (TMLR) | August 2024
  
  Featured Certification
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint プロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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, 巻5
  
  Publication
- Optimal Transport for Causal Discovery
  
  Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
  
  ICLR 2022 | April 2022
  
  Publication プロジェクト
- 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 プロジェクト
- Local Constraint-Based Causal Discovery under Selection Bias
  
  Philip Versteeg, Cheng Zhang, Joris Mooij
  
  Conference on Causal Learning and Reasoning (CLeaR) | January 2022
  
  Publication プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクト
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github プロジェクトプロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 動画 Github プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github プロジェクトプロジェクト
- 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 プロジェクト
- 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 Github プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- A Causal View on Robustness of Neural Networks
  
  Cheng Zhang, Yingzhen Li
  
  June 2019
  
  Publication プロジェクト
- 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
  
  編集者: Kamalika Chaudhuri, Masashi Sugiyama
  
  Publication プロジェクト
- 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 プロジェクト
- Split-door criterion: Identification of causal effects through auxiliary outcomes
  
  Amit Sharma, Jake Hofman, Duncan Watts
  
  Annals of Applied Statistics | December 2018, 巻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 プロジェクト
- Deep Learning with Logged Bandit Feedback
  
  Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
  
  2018 International Conference on Learning Representations | May 2018
  
  Publication Publication プロジェクト
- 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 プロジェクト
- 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
  
  公開日公開日ダウンロードプロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクト
- 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, 巻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 プロジェクト
- 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 プロジェクト
- 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
  
  公開日公開日ダウンロードプロジェクト
- 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
  
  公開日公開日
- 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 プロジェクトプロジェクト
- Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
  
  Amit Sharma
  
  December 2018
  
  Publication プロジェクト
- 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
  
  公開日公開日
- 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 プロジェクトプロジェクト
- 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
  
  公開日公開日
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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
  
  公開日公開日ダウンロードプロジェクト
- Prediction and explanation in social systems
  
  Jake Hofman, Amit Sharma, Duncan Watts
  
  Science | February 2017, 巻355: pp. 486-488
  
  公開日公開日公開日
- 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
  
  公開日公開日

- Robust Root Cause Diagnosis using In-Distribution Interventions
  
  Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
  
  Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, Amit Sharma
  
  2025 International Conference on Learning Representations | April 2025
  
  Publication プロジェクト
- Re-Imagine: Symbolic Benchmark Synthesis for Reasoning Evaluation
  
  Xinnuo Xu, Rachel Lawrence, Kshitij Dubey, Atharva Pandey, Fabian Falck, Risa Ueno, Aditya Nori, Rahul Sharma, Amit Sharma, Javier González
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  Publication PDF
- DEDUCE: Deductive Consistency as a Frame Work to Evaluate LLM Reasoning
  
  Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  PDF
- Teaching Transformers Causal Reasoning through Axiomatic Training
  
  Aniket Vashishtha, Abhinav Kumar, Abbavaram Gowtham Reddy, Kabir Ahuja, Vineeth N. Balasubramanian, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  DOI Publication Publication
- Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
  
  Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma
  
  ICLR 2025 – Workshop on Reasoning and Planning for LLMs | March 2025
  
  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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- Optimal Transport for Causal Discovery
  
  Ruibo Tu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang
  
  ICLR 2022 | April 2022
  
  Publication プロジェクト
- 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 プロジェクト
- Local Constraint-Based Causal Discovery under Selection Bias
  
  Philip Versteeg, Cheng Zhang, Joris Mooij
  
  Conference on Causal Learning and Reasoning (CLeaR) | January 2022
  
  Publication プロジェクト
- 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 プロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 プロジェクト
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github プロジェクトプロジェクト
- 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 プロジェクト
- 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 プロジェクト
- 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 Github プロジェクトプロジェクト
- 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 プロジェクト
- 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
  
  編集者: Kamalika Chaudhuri, Masashi Sugiyama
  
  Publication プロジェクト
- 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 プロジェクト
- Deep Learning with Logged Bandit Feedback
  
  Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
  
  2018 International Conference on Learning Representations | May 2018
  
  Publication Publication プロジェクト
- 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 プロジェクト
- 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
  
  公開日公開日ダウンロードプロジェクト
- 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
  
  公開日公開日
- Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
  
  Emre Kiciman, Robert Osazuwa Ness, Amit Sharma, Chenhao Tan
  
  Transactions on Machine Learning Research (TMLR) | August 2024
  
  Featured Certification
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint プロジェクト
- 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, 巻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 プロジェクトプロジェクト
- Split-door criterion: Identification of causal effects through auxiliary outcomes
  
  Amit Sharma, Jake Hofman, Duncan Watts
  
  Annals of Applied Statistics | December 2018, 巻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, 巻355: pp. 486-488
  
  公開日公開日公開日
- 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 プロジェクト
- A Causal View on Robustness of Neural Networks
  
  Cheng Zhang, Yingzhen Li
  
  June 2019
  
  Publication プロジェクト
- 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 動画 Github プロジェクト
- 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 プロジェクト