DoWhy: Causal Reasoning for Designing and Evaluating Interventions

설립: January 1, 2018

출판물

자료 보기:

- 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
  
  ICLR 2025 | April 2025
  
  Publication
- 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
- 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
  
  Outstanding Certification Finalist
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint
- 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
- 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 프로젝트 프로젝트
- 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
- 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
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github 프로젝트
- DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
  
  Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
  
  MSR-TR-2021-15 | July 2021
  
  글쓴이 ICML 2021 workshop on the Neglected Assumptions in Causal Inference
  
  Publication 프로젝트
- 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
- DoWhy: An End-to-End Library for Causal Inference
  
  Amit Sharma, Emre Kiciman
  
  November 2020
  
  Causal Data Science Meeting (https://causalscience.org/)
  
  Publication 동영상 Github
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github 프로젝트
- Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
  
  Amit Sharma
  
  December 2018
  
  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

- 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
- 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
  
  ICLR 2025 | April 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
  
  Outstanding Certification Finalist
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint
- 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
- 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 프로젝트 프로젝트
- 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
- 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
- DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
  
  Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
  
  MSR-TR-2021-15 | July 2021
  
  글쓴이 ICML 2021 workshop on the Neglected Assumptions in Causal Inference
  
  Publication 프로젝트
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github 프로젝트
- 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
- DoWhy: An End-to-End Library for Causal Inference
  
  Amit Sharma, Emre Kiciman
  
  November 2020
  
  Causal Data Science Meeting (https://causalscience.org/)
  
  Publication 동영상 Github
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github 프로젝트
- Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
  
  Amit Sharma
  
  December 2018
  
  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
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github 프로젝트
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github 프로젝트
- Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
  
  Amit Sharma
  
  December 2018
  
  Publication

- 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
- 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
  
  ICLR 2025 | April 2025
  
  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
- 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 프로젝트 프로젝트
- 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
- 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
- Domain Generalization using Causal Matching
  
  Divyat Mahajan, Shruti Tople, Amit Sharma
  
  International Conference on Machine Learning (ICML) | July 2021
  
  Publication Github 프로젝트
- 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
- Alleviating Privacy Attacks via Causal Learning
  
  Shruti Tople, Amit Sharma, Aditya V. Nori
  
  International Conference on Machine Learning (ICML) | July 2020
  
  Publication Github 프로젝트
- Tutorial on Causal Inference and Counterfactual Reasoning
  
  Emre Kiciman, Amit Sharma
  
  ACM KDD International Conference on Knowledge Discovery and Data Mining | August 2018
  
  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
  
  Outstanding Certification Finalist
  
  Selected for presentation at ICLR 2025
  
  Publication Preprint
- DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
  
  Amit Sharma, Cheng Zhang, Vasilis Syrgkanis, Emre Kiciman
  
  MSR-TR-2021-15 | July 2021
  
  글쓴이 ICML 2021 workshop on the Neglected Assumptions in Causal Inference
  
  Publication 프로젝트
- DoWhy: An End-to-End Library for Causal Inference
  
  Amit Sharma, Emre Kiciman
  
  November 2020
  
  Causal Data Science Meeting (https://causalscience.org/)
  
  Publication 동영상 Github
- Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
  
  Amit Sharma
  
  December 2018
  
  Publication

인원

Amit Sharma

Principal Researcher

Emre Kiciman

Partner Research Manager

DoWhy: Causal Reasoning for Designing and Evaluating Interventions

출판물

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

Robust Root Cause Diagnosis using In-Distribution Interventions

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

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

A Causal AI Suite for Decision-Making

Matching Learned Causal Effects of Neural Networks with Domain Priors

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

Domain Generalization using Causal Matching

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

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

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

Alleviating Privacy Attacks via Causal Learning

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Tutorial on Causal Inference and Counterfactual Reasoning

Robust Root Cause Diagnosis using In-Distribution Interventions

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

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

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

A Causal AI Suite for Decision-Making

Matching Learned Causal Effects of Neural Networks with Domain Priors

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

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

Domain Generalization using Causal Matching

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

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

Alleviating Privacy Attacks via Causal Learning

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Tutorial on Causal Inference and Counterfactual Reasoning

Domain Generalization using Causal Matching

Alleviating Privacy Attacks via Causal Learning

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

Robust Root Cause Diagnosis using In-Distribution Interventions

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

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

A Causal AI Suite for Decision-Making

Matching Learned Causal Effects of Neural Networks with Domain Priors

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

Domain Generalization using Causal Matching

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

Alleviating Privacy Attacks via Causal Learning

Tutorial on Causal Inference and Counterfactual Reasoning

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

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

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

Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables

인원

Amit Sharma

Emre Kiciman