Estimating categorical counterfactuals via deep twin networks


Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, we require knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that the resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but for the case of categorical variables, it remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle positing desirable properties that causal mechanisms should possess and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference—an alternative to the abduction–action–prediction method. We empirically test our approach on diverse real-world and semisynthetic data from medicine, epidemiology and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced


November 2023 | ACM TORS

Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely-Armed Bandit Strategy

Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam

October 2023 | CIKM

Graph Learning for Exploratory Query Suggestions in an Instant Search System

Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan

September 2023 | CLEF

Cem Mil Podcasts: A Spoken Portuguese Document Corpus For Multi-modal, Multi-lingual and Multi-Dialect Information Access Research

Ekaterina Garmash, Edgar Tanaka, Ann Clifton, Joana Correia, Sharmistha Jat, Winstead Zhu, Rosie Jones, Jussi Karlgren