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Workshop

Causal Machine Learning for Real-World Impact

Nick Pawlowski ⋅ Jeroen Berrevoets ⋅ Caroline Uhler ⋅ Kun Zhang ⋅ Mihaela van der Schaar ⋅ Cheng Zhang
Project Page [ Contact: cml.for.impact@gmail.com ]

Abstract

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Schedule

Timezone: America/Los_Angeles
6:30 AM
Opening Remarks
Cheng Zhang ⋅ Mihaela van der Schaar
7:15 AM
Panel Discussion
Cheng Zhang ⋅ Mihaela van der Schaar ⋅ Ilya Shpitser ⋅ Aapo Hyvarinen ⋅ Yoshua Bengio ⋅ Bernhard Schölkopf
8:00 AM
Poster Session
8:00 AM
Coffee Break
9:05 AM
Learning Neural Causal Models
Nan Rosemary Ke
9:30 AM
Discrete Learning Of DAGs Via Backpropagation
Andrew Wren ⋅ Pasquale Minervini ⋅ Luca Franceschi ⋅ Valentina Zantedeschi
9:45 AM
Local Causal Discovery for Estimating Causal Effects
Shantanu Gupta ⋅ David Childers ⋅ Zachary Lipton
10:00 AM
Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
Mayleen Cortez ⋅ Matthew Eichhorn ⋅ Christina Yu
10:15 AM
Hydranet: A Neural Network for the estimation of Multi-valued Treatment Effects
Borja Velasco ⋅ Jesus Cerquides ⋅ Josep Arcos
10:30 AM
Lunch Break
10:30 AM
Poster Session
11:45 AM
Causal ML for medicines R&D
Jim Weatherall
12:45 PM
Coffee Break
12:45 PM
Poster Session
2:00 PM
A Design-Based Riesz Representation Framework For Randomized Experiments
Christopher Harshaw ⋅ Yitan Wang ⋅ Fredrik Sävje
2:30 PM
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
Francesca Raimondi ⋅ Tadhg O'Keeffe ⋅ Andrew Lawrence ⋅ Tamara Stemberga ⋅ Andre Franca ⋅ Maksim Sipos ⋅ Javed Butler ⋅ Shlomo Ben-Haim
2:45 PM
Closing Remarks
Cheng Zhang ⋅ Mihaela van der Schaar
Targeted Causal Elicitation
Nazaal Ibrahim ⋅ ST John ⋅ Zhigao Guo ⋅ Samuel Kaski
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Systems
Parikshit Bansal ⋅ Yashoteja Prabhu ⋅ Emre Kiciman ⋅ Amit Sharma
Making the World More Equal, One Ride at a Time: Studying Public Transportation Initiatives Using Interpretable Causal Inference
Gaurav Rajesh Parikh ⋅ Albert Sun ⋅ Jenny Huang ⋅ Lesia Semenova ⋅ Cynthia Rudin
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta ⋅ Tim G. J. Rudner ⋅ Rowan McAllister ⋅ Adrien Gaidon ⋅ Yarin Gal
A Causal AI Suite for Decision-Making
Emre Kiciman ⋅ Eleanor Dillon ⋅ Darren Edge ⋅ Adam Foster ⋅ Joel Jennings ⋅ Chao Ma ⋅ Robert Ness ⋅ Nick Pawlowski ⋅ Amit Sharma ⋅ Cheng Zhang
Unit Selection: Learning Benefit Function from Finite Population Data
Ang Li ⋅ Song Jiang ⋅ Yizhou Sun ⋅ Judea Pearl
Neural Bayesian Network Understudy
Paloma Rabaey ⋅ Cedric De Boom ⋅ Thomas Demeester
Deep End-to-end Causal Inference
Tomas Geffner ⋅ Javier Antorán ⋅ Adam Foster ⋅ Wenbo Gong ⋅ Chao Ma ⋅ Emre Kiciman ⋅ Amit Sharma ⋅ Angus Lamb ⋅ Martin Kukla ⋅ Nick Pawlowski ⋅ Miltiadis Allamanis ⋅ Cheng Zhang
Contrastive Unsupervised Learning of World Model with Invariant Causal Features
Rudra PK Poudel ⋅ Harit Pandya ⋅ Roberto Cipolla
Toward Fair and Robust Optimal Treatment Regimes
Kwangho Kim ⋅ Jose Zubizarreta
Counterfactual Generation Under Confounding
Abbavaram Gowtham Reddy ⋅ Saloni Dash ⋅ Amit Sharma ⋅ Vineeth N Balasubramanian
A Causal Inference Framework for Network Interference with Panel Data
Sarah Cen ⋅ Anish Agarwal ⋅ Christina Yu ⋅ Devavrat Shah
On Causal Rationalization
Wenbo Zhang ⋅ TONG WU ⋅ Yunlong Wang ⋅ Yong Cai ⋅ Hengrui Cai
Beyond Central Limit Theorem for Higher Order Inference in Batched Bandits
Yechan Park ⋅ Ruohan Zhan ⋅ Nakahiro Yoshida
Valid Inference after Causal Discovery
Paula Gradu ⋅ Tijana Zrnic ⋅ Yixin Wang ⋅ Michael Jordan
Can Large Language Models Build Causal Graphs?
Stephanie Long ⋅ Tibor Schuster ⋅ Alexandre Piche
Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error
Luke Guerdan ⋅ Amanda Coston ⋅ Kenneth Holstein ⋅ Steven Wu
Generalized Synthetic Control Method with State-Space Model
Junzhe Shao ⋅ Mingzhang Yin ⋅ Xiaoxuan Cai ⋅ Linda Valeri
On counterfactual inference with unobserved confounding
Abhin Shah ⋅ Raaz Dwivedi ⋅ Devavrat Shah ⋅ Gregory Wornell
Identifying causes of Pyrocumulonimbus (PyroCb)
Emiliano Diaz ⋅ Kenza Tazi ⋅ Ashwin Braude ⋅ Daniel Okoh ⋅ Kara Lamb ⋅ Duncan Watson-Parris ⋅ Paula Harder ⋅ Nis Meinert
Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise
Wenbo Gong ⋅ Joel Jennings ⋅ Cheng Zhang ⋅ Nick Pawlowski
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
Francesca Raimondi ⋅ Tadhg O'Keeffe ⋅ Hana Chockler ⋅ Andrew Lawrence ⋅ Tamara Stemberga ⋅ Andre Franca ⋅ Maksim Sipos ⋅ Javed Butler ⋅ Shlomo Ben-Haim
Active Bayesian Causal inference
Christian Toth ⋅ Lars Lorch ⋅ Christian Knoll ⋅ Andreas Krause ⋅ Franz Pernkopf ⋅ Robert Peharz ⋅ Julius von Kügelgen
Bounding the Effects of Continuous Treatments for Hidden Confounders
Myrl Marmarelis ⋅ Greg Ver Steeg ⋅ Neda Jahanshad ⋅ Aram Galstyan
Local Causal Discovery for Estimating Causal Effects
Shantanu Gupta ⋅ David Childers ⋅ Zachary Lipton
Partial identification without distributional assumptions
Kirtan Padh ⋅ Jakob Zeitler ⋅ David Watson ⋅ Matt Kusner ⋅ Ricardo Silva ⋅ Niki Kilbertus
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
Mateusz Olko ⋅ Michał Zając ⋅ Aleksandra Nowak ⋅ Nino Scherrer ⋅ Yashas Annadani ⋅ Stefan Bauer ⋅ Łukasz Kuciński ⋅ Piotr Miłoś
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
Qian Wang ⋅ Huanyi Shui ⋅ Thi Tu Trinh Tran ⋅ Milad nezhad ⋅ devesh upadhyay ⋅ Kamran Paynabar ⋅ Anqi He
A kernel balancing approach that scales to big data
Kwangho Kim ⋅ Bijan Niknam ⋅ Jose Zubizarreta
Causal Bandits: Online Decision-Making in Endogenous Settings
Jingwen Zhang ⋅ Yifang Chen ⋅ Amandeep Singh
Machine learning reveals how personalized climate communication can both succeed and backfire
Totte Harinen ⋅ Alexandre Filipowicz ⋅ Shabnam Hakimi ⋅ Rumen Iliev ⋅ Matt Klenk ⋅ Emily Sumner
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman ⋅ Chao Ma ⋅ Agrin Hilmkil ⋅ Joel Jennings ⋅ Cheng Zhang
Causal Discovery using Marginal Likelihood
Anish Dhir ⋅ Mark van der Wilk
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Romain Lopez ⋅ Nataša Tagasovska ⋅ Stephen Ra ⋅ Kyunghyun Cho ⋅ Jonathan Pritchard ⋅ Aviv Regev
Amortized Inference for Causal Structure Learning
Lars Lorch ⋅ Scott Sussex ⋅ Jonas Rothfuss ⋅ Andreas Krause ⋅ Bernhard Schölkopf
Discrete Learning Of DAGs Via Backpropagation
Andrew Wren ⋅ Pasquale Minervini ⋅ Luca Franceschi ⋅ Valentina Zantedeschi
Interventional Causal Representation Learning
Kartik Ahuja ⋅ Yixin Wang ⋅ Divyat Mahajan ⋅ Yoshua Bengio
Synthetic Principle Component Design: Fast Covariate Balancing with Synthetic Controls
Yiping Lu ⋅ Jiajin Li ⋅ Lexing Ying ⋅ Jose Blanchet
Investigating causal understanding in LLMs
Marius Hobbhahn ⋅ Tom Lieberum ⋅ David Seiler
Variational Causal Inference
Yulun Wu ⋅ Layne Price ⋅ Zichen Wang ⋅ Vassilis Ioannidis ⋅ Rob Barton ⋅ George Karypis