Causal query in observational data with hidden variables
- URL: http://arxiv.org/abs/2001.10269v4
- Date: Tue, 24 Nov 2020 05:11:56 GMT
- Title: Causal query in observational data with hidden variables
- Authors: Debo Cheng (1), Jiuyong Li (1), Lin Liu (1), Jixue Liu (1), Kui Yu
(2), and Thuc Duy Le (1) ((1) School of Information Technology and
Mathematical Sciences, University of South Australia (2) School of Computer
Science and Information Engineering, Hefei University of Technology)
- Abstract summary: We develop a theorem for using local search to find a superset of the adjustment variables for causal effect estimation from observational data.
Based on the developed theorem, we propose a data-driven algorithm for causal query.
Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper discusses the problem of causal query in observational data with
hidden variables, with the aim of seeking the change of an outcome when
"manipulating" a variable while given a set of plausible confounding variables
which affect the manipulated variable and the outcome. Such an "experiment on
data" to estimate the causal effect of the manipulated variable is useful for
validating an experiment design using historical data or for exploring
confounders when studying a new relationship. However, existing data-driven
methods for causal effect estimation face some major challenges, including poor
scalability with high dimensional data, low estimation accuracy due to
heuristics used by the global causal structure learning algorithms, and the
assumption of causal sufficiency when hidden variables are inevitable in data.
In this paper, we develop a theorem for using local search to find a superset
of the adjustment (or confounding) variables for causal effect estimation from
observational data under a realistic pretreatment assumption. The theorem
ensures that the unbiased estimate of causal effect is included in the set of
causal effects estimated by the superset of adjustment variables. Based on the
developed theorem, we propose a data-driven algorithm for causal query.
Experiments show that the proposed algorithm is faster and produces better
causal effect estimation than an existing data-driven causal effect estimation
method with hidden variables. The causal effects estimated by the proposed
algorithm are as accurate as those by the state-of-the-art methods using domain
knowledge.
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