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Operationalizing Complex Causes: A Pragmatic View of Mediation

Gultchin, L; Watson, DS; Kusner, MJ; Silva, R; (2021) Operationalizing Complex Causes: A Pragmatic View of Mediation. In: Proceedings of the 38th International Conference on Machine Learning. (pp. pp. 3875-3885). MLResearchPress Green open access

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Abstract

We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we only have access to indirect or \emph{crude} interventions (e.g., enrolling in a writing program, modifying a scene, applying a gene therapy). In this work, we formalize this problem and provide an initial solution. Given a collection of candidate mediators, we propose (a) a two-step method for predicting the causal responses of crude interventions; and (b) a testing procedure to identify mediators of crude interventions. We demonstrate, on a range of simulated and real-world-inspired examples, that our approach allows us to efficiently estimate the effect of crude interventions with limited data from new treatment regimes.

Type: Proceedings paper
Title: Operationalizing Complex Causes: A Pragmatic View of Mediation
Event: 38th International Conference on Machine Learning
Open access status: An open access version is available from UCL Discovery
Publisher version: https://proceedings.mlr.press/v139/gultchin21a.htm...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10130602
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