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Differentiable Causal Backdoor Discovery

Gultchin, L; Kusner, M; Kanade, V; Silva, R; (2020) Differentiable Causal Backdoor Discovery. In: Chiappa, S and Calandra, R, (eds.) Proceedings of the International Conference on Artificial Intelligence and Statistics. PMLR: Online Conference. Green open access

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Abstract

Discovering the causal effect of a decision is critical to nearly all forms of decisionmaking. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is possible to recover the causal effect by using certain observed variables to adjust for the effects of confounders. However, without access to the true causal model, finding this adjustment requires brute-force search. In this work, we present an algorithm that exploits auxiliary variables, similar to instruments, in order to find an appropriate adjustment by a gradient-based optimization method. We demonstrate that it outperforms practical alternatives in estimating the true causal effect, without knowledge of the full causal graph.

Type: Proceedings paper
Title: Differentiable Causal Backdoor Discovery
Event: International Conference on Artificial Intelligence and Statistics
Location: Online
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v108/
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
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.ucl.ac.uk/id/eprint/10110165
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