van der Schaar Lab

NeurIPS 2022 Workshop on Causality for Real-world Impact

This workshop was held at NeurIPS on 2nd December 2023

Causality has a long history, providing it with many principled approaches to identify a causal effect [1-3] (or even distill cause from effect [4]). However, these approaches are often restricted to very specific situations, requiring very specific assumptions [5, 6]. This contrasts heavily with recent advances in machine learning [7-10]. 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.

Speakers

Peter Spirtes

Carnegie Mellon University

Nan Rosemary Ke

DeepMind

Stefan Bauer

KTH

Jim Weatherall

AstraZeneca

Caroline Uhler

MIT

Bernhard Schölkopf

MPI & Amazon

Bin Yu

UC Berkeley

Panellists

Yoshua Bengio

MILA & University of Montreal

Bernhard Schölkopf

MPI & Amazon

Mihaela van der Schaar

University of Cambridge

Aapo Hyvärinen

University of Helsinki

Ilya Shpitser

Johns Hopkins University

Schedule

08:30 – 08:45Opening RemarksCheng Zhang
08:45 – 09:15Invited TalkLearning Causal Structures and Causal
Representations from Data – Peter Spirtes
09:15 – 10:00Panel DiscussionCheng Zhang, Mihaela van der Schaar, Ilya Shpitser,
Aapo Hyvarinen, Yoshua Bengio, Bernhard Schölkopf
10:00 – 10:45Poster Session
10:00 – 10:45Coffee break
10:45 – 11:05Invited TalkCausal Discovery for Real World Applications:
A Case Study – Stefan Bauer
11:05 – 11:25Invited TalkNan Rosemary Ke
11:30 – 11:45Contributed TalkDiscrete Learning Of DAGs Via Backpropagation
11:45 – 12:00Contributed TalkLocal Causal Discovery for Estimating Causal Effects
12:00 – 12:15Contributed TalkExploiting Neighborhood Interference with
Low Order Interactions under Unit Randomized Design
12:15 – 12:30Contributed TalkHydranet: A Neural Network for the estimation
of Multi-valued Treatment Effects
12:30 – 13:45Poster Session
12:30 – 13:45Lunch break
13:45 – 14:15Invited TalkCausal ML for medicines R&D – Jim Weatherall
14:15 – 14:45Invited TalkPlanning and Learning from Interventions in the
Context of Cancer Immunotherapy – Caroline Uhler
14:45 – 15:30Poster Session
14:45 – 15:30Coffee break
15:30 – 16:00Invited TalkStable Discovery of Interpretable Subgroups
via Calibration in Causal Studies – Bin Yu
16:00 – 16:15Contributed TalkA Design-Based Riesz Representation
Framework For Randomized Experiments
16:15 – 16:30Contributed TalkA Causal AI Suite for Decision-Making
16:30 – 16:45Contributed TalkCausal Analysis of the TOPCAT Trial: Spironolactone for
Preserved Cardiac Function Heart Failure
16:45 – 17:00Closing RemarksMihaela van der Schaar, Cheng Zhang

Organisers

Mihaela van der Schaar

University of Cambridge

Cheng Zhang

Microsoft Research

Nick Pawlowski

Microsoft Research

Jeroen Berrevoets

University of Cambridge

Caroline Uhler

MIT

Kun Zhang

Carnegie Mellon University

[1] Jeroen Berrevoets, Zhaozhi Qian, and Mihaela van der Schaar “Causal deep learning.” https://www.vanderschaar-lab.com/causal-deep-learning/ (2022)
[2] Domingo-Fernández, Daniel, et al. “Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery.” PLoS computational biology 18.2 (2022): e1009909.
[3] Kreif, Noémi, and Karla DiazOrdaz. “Machine Learning in Policy Evaluation: New Tools for Causal Inference.” Oxford Research Encyclopedia of Economics and Finance. 2019.
[4] Glymour, Clark, Kun Zhang, and Peter Spirtes. “Review of causal discovery methods based on graphical models.” Frontiers in genetics 10 (2019): 524.
[5] Athey, Susan, and Guido W. Imbens. “The state of applied econometrics: Causality and policy evaluation.” Journal of Economic Perspectives 31.2 (2017): 3-32.
[6] Vandenbroucke, Jan P., Alex Broadbent, and Neil Pearce. “Causality and causal inference in epidemiology: the need for a pluralistic approach.” International journal of epidemiology 45.6 (2016): 1776-1786.
[7] Ke, Nan Rosemary, et al. “Learning to Induce Causal Structure.” arXiv preprint arXiv:2204.04875 (2022).
[8] Geffner, Tomas, et al. “Deep End-to-end Causal Inference.” arXiv preprint arXiv:2202.02195 (2022).
[9] Xia, Kevin, et al. “The causal-neural connection: Expressiveness, learnability, and inference.” Advances in Neural Information Processing Systems 34 (2021).
[10] Pawlowski, Nick, Daniel Coelho de Castro, and Ben Glocker. “Deep structural causal models for tractable counterfactual inference.” Advances in Neural Information Processing Systems 33 (2020): 857-869.