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Causal Inference through a Witness Protection Program

Silva, R; Evans, R; (2014) Causal Inference through a Witness Protection Program. In: Gharamani, Z and Welling, W and Cortes, C and Lawrence, ND and Weinberger, KQ, (eds.) Advances in Neural Information Processing Systems 27 (NIPS 2014). Neural Information Processing Systems Foundation: Montreal, Canada. Green open access

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Abstract

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest weak'' paths in a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of path cancellations'' that will imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice to complement other default tools in observational studies.

Type: Proceedings paper
Title: Causal Inference through a Witness Protection Program
Event: Neural Information Processing Systems 2014
Location: Montreal, Canada
Dates: 08 December 2014 - 13 December 2014
Open access status: An open access version is available from UCL Discovery
Publisher version: http://papers.nips.cc/paper/5602-causal-inference-...
Language: English
Additional information: Copyright © The Authors 2014.
Keywords: causality, Bayesian inference, linear programming
UCL classification: UCL > Provost and Vice Provost Offices
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.ucl.ac.uk/id/eprint/1471795
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