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Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of the causal effect of a treatment on the outcome, or generate a unique estimation of the causal effect, but making strong assumptions on data and having low efficiency. In this paper, we identify a practical problem setting and propose an approach to achieving unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.
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 manipula
Many real-world decision-making tasks require learning casual relationships between a set of variables. Typical causal discovery methods, however, require that all variables are observed, which might not be realistic in practice. Unfortunately, in th
Causal effect estimation from observational data is an important but challenging problem. Causal effect estimation with unobserved variables in data is even more difficult. The challenges lie in (1) whether the causal effect can be estimated from obs
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and nontreated groups.
An important problem in causal inference is to break down the total effect of treatment into different causal pathways and quantify the causal effect in each pathway. Causal mediation analysis (CMA) is a formal statistical approach for identifying an