3rd Workshop on Causal Inference and Machine Learning in Practice

Schedule

  • Room 601, Metro Toronto Convention Centre, 255 Front St W, Toronto, ON M5V 2W6, Canada map
  • Date: Monday, August 4th, 2025
  • Time: 8:00 AM - 12:00 PM

Abstract

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference with machine learning. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for applying and evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with machine learning algorithms.

Building on the success of the previous editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning.

Paper Submission

Please submit your paper to the CMT portal site, and check the Call for Paper page for details on important dates and submission guidelines.

Program

Title Speaker Time (Duration) Link
Introduction Jing Pan (Snap Inc.) 8:00 - 8:05am (5m)  
[Invited Talk] Applications of Causal Machine Learning in Building a Unified Metric System Wenjing Zheng (Roblox) 8:05 - 8:25am (20m) Slides
Offline Contextual Bandits in the Presence of New Actions Yuta Saito* (Cornell University), Ren Kishimoto (Institute of Science Tokyo), Tatsuhiro Shimizu, Yusuke Narita (Yale University), Kazuki Kawamura, Takanori Muroi, Yuki Sasamoto, Kei Tateno, Takuma Udagawa (Sony Group Corporation) 8:25 - 8:35am (10m) Paper
Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation Sang Su Lee*, Vineeth Loganathan, Vijay Raghavan (Thumbtack, Inc.) 8:35 - 8:45am (10m) Paper
[Invited Talk] Sparse Causal Learning: Challenges and Opportunities Linbo Wang (University of Toronto) 8:50 - 9:10am (20m) Slides
CIPHER: Causal Intent Plug-in Framework for the Mitigation Plug-in Framework for then of Historical Exposure Bias in Recommender Systems Sanghyeon Lee*, Yeonghwan Jeon, Hyuncheol Jo, Byoung-Ki Jeon (LG Uplus) 9:10 - 9:20am (10m) Paper
Does Residuals-on-Residuals Regression Produce Representative Estimates of Causal Effects? Winston Chou (Netflix)*, Apoorva Lal (Amazon) 9:20 - 9:30am (10m) Paper
Coffee Break & Poster Session   9:30 - 10:00am  
[Invited Talk] Efficient Difference-in-Differences and Event Study Estimators Pedro Sant’Anna (Emory University) 10:00 - 10:20am (20m) Slides
Causal Inference under Threshold Manipulation: A Bayesian Mixture Approach Kohsuke Kubota (NTT DOCOMO, INC)*, Shonosuke Sugasawa (Keio University) 10:20 - 10:30am (10m) Paper
I See, Therefore I Do: Estimating Causal Effects for Image Treatments Ravi Kolla*, Abhinav Thorat, Niranjan Pedanekar (Sony Research India) 10:30 - 10:40am (10m) Paper
An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making Javier Moral Hernández*, Clara Higuera‑Cabañes, Álvaro Ibráin (BBVA, AI Factory) 10:40 - 10:50am (10m) Paper
[Invited Talk] Engineering Patterns in Causal Inference Jeffrey Wong (Airbnb) 10:55 - 11:15am (20m) Slides
A Debiased Machine Learning Framework for Optimizing Price Promotion within E-commerce Bo Zhang*, Sergazy Nurbavliyev, Vaidyanath Areyur Shanthakumar, Stephen Merrill, Raymond J. Pan, Komson Chanprapan (Beyond Inc, Midvale, Utah, USA) 11:25 - 11:35am (10m) Paper
Auditable Surge Planning: Network-Aware Causal Inference Meets Prescriptive Optimization Sanjay Patnala*, Vedika Lakhanpal (Georgia Institute of Technology) 11:35 - 11:45am (10m) Paper
IMPACT: An Inference-Driven Modeling Framework for Cost-Effective Incentive Allocation in Service Operations Yiheng An*, Jia Li, Jeffrey Camm (Wake Forest University), Liang Hu, Qinqin Zhuge, Bingxin Jia (Alibaba Group) 11:45 - 11:55am (10m) Paper
Closing Zeyu Zheng (University of California Berkeley) 11:55am-12:00pm (5m)  

Invited Speakers

Accepted Papers

For Oral Presentation

  1. A Debiased Machine Learning Framework for Optimizing Price Promotion within E-commerce; Bo Zhang*, Sergazy Nurbavliyev, Vaidyanath Areyur Shanthakumar, Stephen Merrill, Raymond J. Pan, Komson Chanprapan (Beyond Inc, Midvale, Utah, USA); Paper
  2. An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making; Javier Moral Hernández*, Clara Higuera‑Cabañes, Álvaro Ibráin (BBVA, AI Factory); Paper
  3. Auditable Surge Planning: Network-Aware Causal Inference Meets Prescriptive Optimization; Sanjay Patnala*, Vedika Lakhanpal (Georgia Institute of Technology); Paper
  4. Causal Inference under Threshold Manipulation: A Bayesian Mixture Approach; Kohsuke Kubota (NTT DOCOMO, INC)*, Shonosuke Sugasawa (Keio University); Paper
  5. CIPHER: Causal Intent Plug-in Framework for the Mitigation of Historical Exposure Bias in Recommender Systems; Sanghyeon Lee*, Yeonghwan Jeon, Hyuncheol Jo, Byoung-Ki Jeon (LG Uplus); Paper
  6. Does Residuals-on-Residuals Regression Produce Representative Estimates of Causal Effects?; Winston Chou (Netflix)*, Apoorva Lal (Amazon); Paper
  7. Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation; Sang Su Lee*, Vineeth Loganathan, Vijay Raghavan (Thumbtack, Inc.); Paper
  8. I See, Therefore I Do: Estimating Causal Effects for Image Treatments; Ravi Kolla*, Abhinav Thorat, Niranjan Pedanekar (Sony Research India); Paper
  9. IMPACT: An Inference-Driven Modeling Framework for Cost-Effective Incentive Allocation in Service Operations; Yiheng An*, Jia Li, Jeffrey Camm (Wake Forest University), Liang Hu, Qinqin Zhuge, Bingxin Jia (Alibaba Group); Paper
  10. Offline Contextual Bandits in the Presence of New Actions; Yuta Saito* (Cornell University), Ren Kishimoto (Institute of Science Tokyo), Tatsuhiro Shimizu, Yusuke Narita (Yale University), Kazuki Kawamura, Takanori Muroi, Yuki Sasamoto, Kei Tateno, Takuma Udagawa (Sony Group Corporation); Paper

For Poster Presentation

  1. Causal Machine Learning for Promotions: Industry Evidence and Applications; Muxi Xu (DoorDash)*, Kun Hu (DoorDash), Sudeep Das (DoorDash), Bruce Wang (DoorDash); Paper
  2. Estimation of Single and Synergistic Treatment Effects under Multiple Treatments with Deep Neural Networks; Yuki Murakami (NTT DOCOMO, INC.)*, Kohsuke Kubota (NTT DOCOMO, INC.), Takumi Hattori (NTT DOCOMO, INC.), Keiichi Ochiai (Yokohama City University; NTT DOCOMO, INC.); Paper
  3. Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models; Haochen Song (University of Toronto)*, Dominik Hofer (Ludwig Boltzmann Institute for Digital Health and Prevention), Rania Islambouli (Ludwig Boltzmann Institute for Digital Health and Prevention), Laura Hawkins (University of Toronto), Ananya Bhattacharjee (University of Toronto), Meredith Franklin (University of Toronto), Joseph Williams (University of Toronto); Paper
  4. Leveraging Large Language Models and Knowledge Graphs for Disease Theory Exploration and Causal Analysis; Isak Midtvedt (OsloMET), Shanshan Jiang (SINTEF)*, Dumitru Roman (SINTEF); Paper
  5. Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference; Ilias Tsoumas (National Observatory of Athens)*, Dimitrios Bormpoudakis (National Observatory of Athens), Vasileios Sitokonstantinou (University of Valencia), Athanasios Askitopoulos (National Observatory of Athens), Andreas Kalogeras (National Observatory of Athens), Charalampos (Haris) Kontoes (National Observatory of Athens), Ioannis Athanasiadis (Wageningen University and Research); Paper

Organizers

  • Chu Wang, Amazon
  • Jingshen Wang, UC Berkeley
  • Sourav Sinha, Instacart
  • Sichao Yin, Instacart
  • Yingfei Wang, University of Washington
  • Zeyu Zheng, UC Berkeley, Amazon - main contact
  • Jing Pan, Snap - main contact
  • Paul Lo, Snap

The CausalML Team

  • Huigang Chen, Google
  • Jeong-Yoon Lee, Uber
  • Roland Stevenson, Consultant
  • Totte Harinen, AirBnB
  • Yifeng Wu, Uber
  • Zhenyu Zhao, Roblox

Past Workshop