Workshop
Causal Machine Learning for Real-World Impact
Nick Pawlowski · Jeroen Berrevoets · Caroline Uhler · Kun Zhang · Mihaela van der Schaar · Cheng Zhang
Room 295 - 296
Fri 2 Dec, 6:30 a.m. PST
Causality has a long history, providing it with many principled approaches to identify a causal effect (or even distill cause from effect). However, these approaches are often restricted to very specific situations, requiring very specific assumptions. This contrasts heavily with recent advances in machine learning. Real-world problems aren’t granted the luxury of making strict assumptions, yet still require causal thinking to solve. Armed with the rigor of causality, and the can-do-attitude of machine learning, we believe the time is ripe to start working towards solving real-world problems.
Chat is not available.
Timezone: America/Los_Angeles
Schedule
Fri 6:30 a.m. - 6:45 a.m.
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Opening Remarks
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Opening Remarks
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SlidesLive Video |
Cheng Zhang · Mihaela van der Schaar 🔗 |
Fri 6:45 a.m. - 7:15 a.m.
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Learning Causal Structures and Causal Representations from Data
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Talk
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SlidesLive Video |
Peter Spirtes 🔗 |
Fri 7:15 a.m. - 8:00 a.m.
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Panel Discussion
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Panel Discussion
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SlidesLive Video |
Cheng Zhang · Mihaela van der Schaar · Ilya Shpitser · Aapo Hyvarinen · Yoshua Bengio · Bernhard Schölkopf 🔗 |
Fri 8:00 a.m. - 8:45 a.m.
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Poster Session
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Poster Session
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Fri 8:00 a.m. - 8:30 a.m.
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Coffee Break
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Fri 8:45 a.m. - 9:05 a.m.
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Causal Discovery for Real World Applications: A Case Study
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Talk
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SlidesLive Video |
Stefan Bauer 🔗 |
Fri 9:05 a.m. - 9:25 a.m.
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Learning Neural Causal Models
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Talk
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SlidesLive Video |
Nan Rosemary Ke 🔗 |
Fri 9:30 a.m. - 9:45 a.m.
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Discrete Learning Of DAGs Via Backpropagation
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Talk
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SlidesLive Video |
Andrew Wren · Pasquale Minervini · Luca Franceschi · Valentina Zantedeschi 🔗 |
Fri 9:45 a.m. - 10:00 a.m.
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Local Causal Discovery for Estimating Causal Effects
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Talk
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SlidesLive Video |
Shantanu Gupta · David Childers · Zachary Lipton 🔗 |
Fri 10:00 a.m. - 10:15 a.m.
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Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
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Talk
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SlidesLive Video |
Mayleen Cortez · Matthew Eichhorn · Christina Yu 🔗 |
Fri 10:15 a.m. - 10:30 a.m.
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Hydranet: A Neural Network for the estimation of Multi-valued Treatment Effects
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Talk
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SlidesLive Video |
Borja Velasco · Jesus Cerquides · Josep Arcos 🔗 |
Fri 10:30 a.m. - 11:45 a.m.
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Lunch Break
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Lunch Break
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Fri 10:30 a.m. - 11:45 a.m.
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Poster Session
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Poster Session
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Fri 11:45 a.m. - 12:15 p.m.
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Causal ML for medicines R&D
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Talk
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SlidesLive Video |
Jim Weatherall 🔗 |
Fri 12:15 p.m. - 12:45 p.m.
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Planning and Learning from Interventions in the Context of Cancer Immunotherapy
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Talk
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SlidesLive Video |
Caroline Uhler 🔗 |
Fri 12:45 p.m. - 1:30 p.m.
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Coffee Break
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Fri 12:45 p.m. - 1:30 p.m.
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Poster Session
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Poster Session
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Fri 1:30 p.m. - 2:00 p.m.
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Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies
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Talk
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SlidesLive Video |
Bin Yu 🔗 |
Fri 2:00 p.m. - 2:15 p.m.
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A Design-Based Riesz Representation Framework For Randomized Experiments
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Talk
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SlidesLive Video |
Christopher Harshaw · Yitan Wang · Fredrik Sävje 🔗 |
Fri 2:15 p.m. - 2:30 p.m.
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A Causal AI Suite for Decision-Making
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Talk
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SlidesLive Video |
Emre Kiciman 🔗 |
Fri 2:30 p.m. - 2:45 p.m.
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Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
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Talk
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SlidesLive Video |
Francesca Raimondi · Tadhg O'Keeffe · Andrew Lawrence · Tamara Stemberga · Andre Franca · Maksim Sipos · Javed Butler · Shlomo Ben-Haim 🔗 |
Fri 2:45 p.m. - 3:00 p.m.
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Closing Remarks
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Closing Remarks
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SlidesLive Video |
Cheng Zhang · Mihaela van der Schaar 🔗 |
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Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance ( Poster ) > link | Aengus Lynch · Jean Kaddour · Ricardo Silva 🔗 |
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Targeted Causal Elicitation ( Poster ) > link | Nazaal Ibrahim · ST John · Zhigao Guo · Samuel Kaski 🔗 |
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Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Systems ( Poster ) > link | Parikshit Bansal · Yashoteja Prabhu · Emre Kiciman · Amit Sharma 🔗 |
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Exploiting Selection Bias on Underspecified Tasks in Large Language Models ( Poster ) > link | Emily McMilin 🔗 |
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Making the World More Equal, One Ride at a Time: Studying Public Transportation Initiatives Using Interpretable Causal Inference ( Poster ) > link | Gaurav Rajesh Parikh · Albert Sun · Jenny Huang · Lesia Semenova · Cynthia Rudin 🔗 |
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Non-Stationary Causal Bandits ( Poster ) > link | REDA ALAMI 🔗 |
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Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? ( Poster ) > link | Gunshi Gupta · Tim G. J. Rudner · Rowan McAllister · Adrien Gaidon · Yarin Gal 🔗 |
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A Causal AI Suite for Decision-Making ( Poster ) > link | Emre Kiciman · Eleanor Dillon · Darren Edge · Adam Foster · Joel Jennings · Chao Ma · Robert Ness · Nick Pawlowski · Amit Sharma · Cheng Zhang 🔗 |
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Unit Selection: Learning Benefit Function from Finite Population Data ( Poster ) > link | Ang Li · Song Jiang · Yizhou Sun · Judea Pearl 🔗 |
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Neural Bayesian Network Understudy ( Poster ) > link | Paloma Rabaey · Cedric De Boom · Thomas Demeester 🔗 |
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Hydranet: A Neural Network for the estimation of Multi-valued Treatment Effects ( Poster ) > link | Borja Velasco · Jesus Cerquides · Josep Arcos 🔗 |
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Deep End-to-end Causal Inference ( Poster ) > link |
12 presentersTomas Geffner · Javier Antorán · Adam Foster · Wenbo Gong · Chao Ma · Emre Kiciman · Amit Sharma · Angus Lamb · Martin Kukla · Nick Pawlowski · Miltiadis Allamanis · Cheng Zhang |
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Contrastive Unsupervised Learning of World Model with Invariant Causal Features ( Poster ) > link | Rudra PK Poudel · Harit Pandya · Roberto Cipolla 🔗 |
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Toward Fair and Robust Optimal Treatment Regimes ( Poster ) > link | Kwangho Kim · Jose Zubizarreta 🔗 |
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Counterfactual Generation Under Confounding ( Poster ) > link | Abbavaram Gowtham Reddy · Saloni Dash · Amit Sharma · Vineeth N Balasubramanian 🔗 |
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A Causal Inference Framework for Network Interference with Panel Data ( Poster ) > link | Sarah Cen · Anish Agarwal · Christina Yu · Devavrat Shah 🔗 |
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Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests ( Poster ) > link | Erica Cai · Andrew McGregor · David Jensen 🔗 |
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Identifying Causal Effects Of Exercise On Irregular Heart Rhythm Events Using Wearable Device Data ( Poster ) > link | Lauren Hannah · Adam Bouyamourn 🔗 |
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On Causal Rationalization ( Poster ) > link | Wenbo Zhang · TONG WU · Yunlong Wang · Yong Cai · Hengrui Cai 🔗 |
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The Counterfactual-Shapley Value: Attributing Change in System Metrics ( Poster ) > link | Amit Sharma · Hua Li · Jian Jiao 🔗 |
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Beyond Central Limit Theorem for Higher Order Inference in Batched Bandits ( Poster ) > link | Yechan Park · Ruohan Zhan · Nakahiro Yoshida 🔗 |
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Valid Inference after Causal Discovery ( Poster ) > link | Paula Gradu · Tijana Zrnic · Yixin Wang · Michael Jordan 🔗 |
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Can Large Language Models Build Causal Graphs? ( Poster ) > link | Stephanie Long · Tibor Schuster · Alexandre Piche 🔗 |
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Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error ( Poster ) > link | Luke Guerdan · Amanda Coston · Kenneth Holstein · Steven Wu 🔗 |
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Causal Estimation for Text Data with (Apparent) Overlap Violations ( Poster ) > link | Lin Gui · Victor Veitch 🔗 |
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Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow ( Poster ) > link | Felipe Giori · Flavio Figueiredo 🔗 |
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Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength ( Poster ) > link | Jiageng Zhu · Hanchen Xie · Wael Abd-Almageed 🔗 |
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Mitigating input-causing confounding in multimodal learning via the backdoor adjustment ( Poster ) > link | Taro Makino · Krzysztof Geras · Kyunghyun Cho 🔗 |
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Generalized Synthetic Control Method with State-Space Model ( Poster ) > link | Junzhe Shao · Mingzhang Yin · Xiaoxuan Cai · Linda Valeri 🔗 |
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On counterfactual inference with unobserved confounding ( Poster ) > link | Abhin Shah · Raaz Dwivedi · Devavrat Shah · Gregory Wornell 🔗 |
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Identifying causes of Pyrocumulonimbus (PyroCb) ( Poster ) > link | Emiliano Diaz · Kenza Tazi · Ashwin Braude · Daniel Okoh · Kara Lamb · Duncan Watson-Parris · Paula Harder · Nis Meinert 🔗 |
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Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise ( Poster ) > link | Wenbo Gong · Joel Jennings · Cheng Zhang · Nick Pawlowski 🔗 |
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Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure ( Poster ) > link | Francesca Raimondi · Tadhg O'Keeffe · Hana Chockler · Andrew Lawrence · Tamara Stemberga · Andre Franca · Maksim Sipos · Javed Butler · Shlomo Ben-Haim 🔗 |
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Conditional differential measurement error: partial identifiability and estimation ( Poster ) > link | Pengrun Huang · Maggie Makar 🔗 |
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Active Bayesian Causal inference ( Poster ) > link | Christian Toth · Lars Lorch · Christian Knoll · Andreas Krause · Franz Pernkopf · Robert Peharz · Julius von Kügelgen 🔗 |
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Bounding the Effects of Continuous Treatments for Hidden Confounders ( Poster ) > link | Myrl Marmarelis · Greg Ver Steeg · Neda Jahanshad · Aram Galstyan 🔗 |
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Local Causal Discovery for Estimating Causal Effects ( Poster ) > link | Shantanu Gupta · David Childers · Zachary Lipton 🔗 |
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Partial identification without distributional assumptions ( Poster ) > link | Kirtan Padh · Jakob Zeitler · David Watson · Matt Kusner · Ricardo Silva · Niki Kilbertus 🔗 |
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Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery ( Poster ) > link | Mateusz Olko · Michał Zając · Aleksandra Nowak · Nino Scherrer · Yashas Annadani · Stefan Bauer · Łukasz Kuciński · Piotr Miłoś 🔗 |
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A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis ( Poster ) > link | Qian Wang · Huanyi Shui · Thi Tu Trinh Tran · Milad nezhad · devesh upadhyay · Kamran Paynabar · Anqi He 🔗 |
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A kernel balancing approach that scales to big data ( Poster ) > link | Kwangho Kim · Bijan Niknam · Jose Zubizarreta 🔗 |
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Causal Bandits: Online Decision-Making in Endogenous Settings ( Poster ) > link | Jingwen Zhang · Yifang Chen · Amandeep Singh 🔗 |
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Rethinking Neural Relational Inference for Granger Causal Discovery ( Poster ) > link | Stefanos Bennett · Rose Yu 🔗 |
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Machine learning reveals how personalized climate communication can both succeed and backfire ( Poster ) > link | Totte Harinen · Alexandre Filipowicz · Shabnam Hakimi · Rumen Iliev · Matt Klenk · Emily Sumner 🔗 |
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Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning ( Poster ) > link | Matthew Ashman · Chao Ma · Agrin Hilmkil · Joel Jennings · Cheng Zhang 🔗 |
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Causal Discovery using Marginal Likelihood ( Poster ) > link | Anish Dhir · Mark van der Wilk 🔗 |
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Deep Structural Causal Modelling of the Clinical and Radiological Phenotype of Alzheimer’s Disease ( Poster ) > link | Ahmed Abdulaal · Daniel C. Castro · Daniel Alexander 🔗 |
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Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling ( Poster ) > link | Romain Lopez · Nataša Tagasovska · Stephen Ra · Kyunghyun Cho · Jonathan Pritchard · Aviv Regev 🔗 |
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Amortized Inference for Causal Structure Learning ( Poster ) > link | Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf 🔗 |
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Discrete Learning Of DAGs Via Backpropagation ( Poster ) > link | Andrew Wren · Pasquale Minervini · Luca Franceschi · Valentina Zantedeschi 🔗 |
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Interventional Causal Representation Learning ( Poster ) > link | Kartik Ahuja · Yixin Wang · Divyat Mahajan · Yoshua Bengio 🔗 |
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Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design ( Poster ) > link | Mayleen Cortez · Matthew Eichhorn · Christina Yu 🔗 |
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Synthetic Principle Component Design: Fast Covariate Balancing with Synthetic Controls ( Poster ) > link | Yiping Lu · Jiajin Li · Lexing Ying · Jose Blanchet 🔗 |
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Investigating causal understanding in LLMs ( Poster ) > link | Marius Hobbhahn · Tom Lieberum · David Seiler 🔗 |
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A Large-Scale Observational Study of the Causal Effects of a Behavioral Health Nudge ( Poster ) > link | Achille Nazaret · Guillermo Sapiro 🔗 |
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Variational Causal Inference ( Poster ) > link | Yulun Wu · Layne Price · Zichen Wang · Vassilis Ioannidis · Rob Barton · George Karypis 🔗 |