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Operationalizing Complex Causes: A Pragmatic View of Mediation
Limor Gultchin · David Watson · Matt J. Kusner · Ricardo Silva

Thu Jul 22 08:50 PM -- 08:55 PM (PDT) @
in Causal Learning »

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.

Keywords: Causal Inference

Author Information

Limor Gultchin (University of Oxford)
David Watson (University College London)
Matt J. Kusner (University College London)
Ricardo Silva (University College London)

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